Fine-Tuning Strategies for Materials Foundation Models: A Guide for Biomedical and Clinical Research

Abstract

This article provides a comprehensive guide for researchers and drug development professionals on fine-tuning materials foundation models. Foundation models, pre-trained on vast and diverse atomistic datasets, offer a powerful starting point for simulating complex biological and materials systems. We explore the core concepts of these models and detail targeted fine-tuning strategies that achieve high accuracy with minimal, system-specific data. The article covers practical methodologies, including parameter-efficient fine-tuning and integrated software platforms, addresses common challenges like catastrophic forgetting and data scarcity, and presents rigorous validation frameworks. By synthesizing the latest research, this guide aims to empower scientists to reliably adapt these advanced AI tools for applications in drug discovery, biomaterials development, and clinical pharmacology.

What Are Materials Foundation Models and Why Fine-Tune Them?

The field of atomistic simulation is undergoing a profound transformation, driven by the emergence of AI-based foundation models. These models represent a fundamental shift from traditional, narrowly focused machine-learned interatomic potentials (MLIPs) towards large-scale, pre-trained models that capture the broad principles of atomic interactions across chemical space. The core idea is to leverage data and parameter scaling laws, inspired by the success of large language models, to create a foundational understanding of chemistry and materials that can be efficiently adapted to a wide range of downstream tasks with minimal additional data [1]. This paradigm separates the costly representation learning phase from application-specific fine-tuning, offering unprecedented efficiency and transferability compared to training models from scratch for each new system [1] [2].

A critical distinction must be made between universal potentials and true foundation models. Universal potentials, such as MACE-MP-0, are models trained to be broadly applicable force fields for systems across the periodic table, typically at one level of theory [1] [2]. While immensely valuable, they are supervised to perform one specific task: predict energy and force labels. A true atomistic foundation model, in contrast, exhibits three defining characteristics: (1) superior performance across diverse downstream tasks compared to task-specific models, (2) compliance with heuristic scaling laws where performance improves with increased model parameters and training data, and (3) emergent capabilities—solving tasks that appeared impossible at smaller scales, such as predicting higher-quality CCSD(T) data from DFT training data [1].

Architectural Foundations and Key Models

Atomistic foundation models are built on geometric machine learning architectures that inherently respect the physical symmetries of atomic systems, including translation, rotation, and permutation invariance. Most current models employ graph neural network (GNN) architectures where atoms represent nodes and bonds represent edges in a graph [3] [2]. These models incorporate increasingly sophisticated advancements including many-body interactions, equivariant features, and transformer-like architectures to capture complex atomic environments [2].

Table 1: Prominent Atomistic Foundation Models and Their Specifications

Model	Release Year	Parameters	Training Data Size	Primary Training Objective
MACE-MP-0	2023	4.69M	1.58M structures	Energy, forces, stress
GNoME	2023	16.2M	16.2M structures	Energy, forces
MatterSim-v1	2024	4.55M	17M structures	Energy, forces, stress
ORB-v1	2024	25.2M	32.1M structures	Denoising + energy, forces, stress
JMP-L	2024	235M	120M structures	Energy, forces
EquiformerV2-M	2024	86.6M	102M structures	Energy, forces, stress

These models learn robust, transferable representations of atomic environments through pre-training on massive, diverse datasets comprising inorganic crystals, molecular systems, reactive mixtures, and more [4]. The training incorporates careful homogenization of reference energies and uniform treatment of dispersion corrections to ensure consistency across chemical space [4].

Fine-Tuning Methodologies and Protocols

Frozen Transfer Learning Protocol

The "frozen transfer learning" approach has emerged as a particularly effective fine-tuning strategy for atomistic foundation models. This method involves controlled freezing of neural network layers during fine-tuning, where parameters in specific layers remain fixed while only a subset of layers are updated [3].

Application Protocol: Implementing Frozen Transfer Learning

• Foundation Model Selection: Choose an appropriate pre-trained model (e.g., MACE-MP "small," "medium," or "large") based on your computational resources and accuracy requirements [3].

• Layer Freezing Strategy: Implement a progressive unfreezing approach:

  • Freeze all layers except the readout functions (designated as f6 configuration)
  • Gradually unfreeze deeper layers: product layer (f5), then interaction parameters (f4)
  • Empirical studies show optimal performance typically with 4 frozen layers (f4 configuration) [3]

• Data Preparation: Curate a task-specific dataset of atomic structures with corresponding target properties (energies, forces). For reactive surface chemistry, several hundred configurations often suffice [3].

• Model Training:

  • Utilize specialized software implementations such as the mace-freeze patch for the MACE software suite [3]
  • Maintain the original model architecture while restricting gradient computation to non-frozen layers
  • Employ standard optimization techniques (Adam, L-BFGS) with reduced learning rates for fine-tuning

• Validation: Assess performance on held-out configurations, comparing energy and force root mean squared error (RMSE) against both the foundation model and from-scratch trained models [3].

This protocol demonstrates remarkable data efficiency, with frozen transfer learned models achieving accuracy comparable to from-scratch models trained on 5x more data [3]. For instance, MACE-MP-f4 models trained on just 20% of a dataset (664 configurations) showed similar accuracy to from-scratch models trained on the entire dataset (3376 configurations) [3].

Integrated Fine-Tuning Platforms

Comprehensive platforms like MatterTune provide integrated environments for fine-tuning atomistic foundation models. MatterTune offers a modular framework consisting of four core components: a model subsystem, data subsystem, trainer subsystem, and application subsystem [2]. This platform supports multiple state-of-the-art foundation models including ORB, MatterSim, JMP, MACE, and EquiformerV2, enabling researchers to fine-tune models for diverse materials informatics tasks beyond force fields, such as property prediction and materials screening [2].

Foundation Model Fine-Tuning Workflow

Experimental Validation and Performance Metrics

Quantitative Performance Assessment

Rigorous benchmarking establishes the accuracy and domain of universality for fine-tuned foundation models. Large-scale assessments across thousands of materials show that leading models can reproduce energies, forces, lattice parameters, elastic properties, and phonon spectra with remarkable accuracy [4].

Table 2: Performance Metrics of Fine-Tuned Foundation Models on Challenging Datasets

System	Fine-Tuning Method	Training Data Size	Energy RMSE	Force RMSE	Comparative Performance
H₂/Cu surfaces	MACE-MP-f4 (frozen)	664 configurations (20%)	< 5 meV/atom	~30 meV/Å	Matches from-scratch model trained on 3376 configurations [3]
Ternary alloys	MACE-MP-f4 (frozen)	10-20% of full dataset	Comparable to full training	Comparable to full training	Achieves similar accuracy with 80-90% less data [3]
Various materials	UMLPs fine-tuned	System-specific	~0.044 eV/atom (formation energies)	Several meV/Å	Reproduces DFT-level accuracy for diverse properties [4]

For particularly challenging properties like mixing enthalpies in alloys, where small energy differences are critical, foundation models fine-tuned with system-specific data can correct initial errors and restore correct thermodynamic trends [4]. Similarly, for surface systems—typically underrepresented in broad training sets—targeted fine-tuning significantly reduces errors correlated with descriptor-space distance from the original training data [4].

Advanced Adaptation Strategies

Beyond basic fine-tuning, several advanced adaptation strategies enhance foundation model performance:

Predictor-Corrector Fine-Tuning: Pre-trained universal machine-learned potentials provide robust initializations, and fine-tuning rapidly improves accuracy on task-specific datasets, often outperforming models trained from scratch and reducing outlier errors in lattice parameters, defect energies, and elastic constants [4].

Active Learning Integration: In global optimization and structure search, the combination of a universal surrogate with sparse Gaussian Process Regression models enables iterative, on-the-fly improvement. This approach, coupled with structure search algorithms like replica exchange, leads to robust identification of DFT global minima even in challenging systems [4].

Multi-Head Fine-Tuning: This approach maintains transferability across systems represented in the original pre-training dataset while allowing training on data from multiple levels of electronic structure theory, addressing the challenge of catastrophic forgetting during fine-tuning [3].

Research Reagent Solutions: Essential Tools for Implementation

Table 3: Key Software and Computational Tools for Atomistic Foundation Models

Tool/Platform	Type	Primary Function	Supported Models
MatterTune	Fine-tuning platform	Modular framework for fine-tuning atomistic FMs	ORB, MatterSim, JMP, MACE, EquiformerV2 [2]
MACE software suite	MLIP infrastructure	Training and fine-tuning MACE-based models	MACE-MP and variants [3]
mace-freeze patch	Specialized tool	Implements frozen transfer learning for MACE	MACE-MP foundation models [3]
MedeA Environment	Commercial platform	Integrated workflows for MLP generation and application	VASP-integrated MLPs [5] [6]
ALCF Supercomputers	HPC infrastructure	Large-scale training of foundation models	Custom models (e.g., battery electrolytes) [7]

Atomistic Foundation Model Research Ecosystem

The development of atomistic foundation models represents a paradigm shift in computational materials science and chemistry. By distinguishing these models from mere universal potentials and establishing robust fine-tuning protocols, researchers can leverage their full potential as adaptable, specialized tools. The frozen transfer learning methodology, in particular, offers a data-efficient pathway to achieving chemical accuracy for challenging systems like reactive surfaces and complex alloys.

Future developments will likely focus on enhanced architectural principles incorporating explicit long-range interactions and polarizability [4], more sophisticated continual learning approaches to prevent catastrophic forgetting, and improved benchmarking across diverse chemical domains. As these models continue to evolve, they promise to dramatically accelerate materials discovery and molecular design across pharmaceuticals, energy storage, and beyond [8] [9] [7].

The development of Foundation Models (FMs) represents a paradigm shift across scientific domains, from atomistic simulations in materials science to biomarker detection in oncology. These large-scale, pretrained models achieve remarkable generalization by learning universal representations from extensive datasets. However, a fundamental challenge emerges: the accuracy-transferability trade-off. This core conflict arises when enhancing a model's accuracy for a specific, high-fidelity task compromises its performance across diverse, out-of-distribution scenarios. In materials science, FMs pretrained on millions of generalized gradient approximation (GGA) density functional theory (DFT) calculations demonstrate high transferability but exhibit consistent systematic errors, such as energy and force underprediction [10]. Conversely, migrating these models to higher-accuracy functionals like meta-GGAs (e.g., r2SCAN) improves accuracy but introduces transferability challenges due to significant energy scale shifts and poor label correlation between fidelity levels [10]. Understanding and managing this trade-off is critical for deploying robust FMs in real-world research and development, where both precision and adaptability are required.

Quantitative Benchmarking of the Trade-off

The accuracy-transferability trade-off manifests quantitatively across different domains. The following tables summarize key performance metrics from recent studies, highlighting the performance gaps between internal and external validation, a key indicator of transferability.

Table 1: Performance of a Fine-Tuned Pathology Foundation Model (EAGLE) for EGFR Mutation Detection in Lung Cancer [11]

Validation Setting	Dataset Description	Area Under the Curve (AUC)	Notes
Internal Validation	1,742 slides from MSKCC	0.847	Baseline performance on primary samples was higher (AUC 0.90)
External Validation	1,484 slides from 4 institutions	0.870	Demonstrates strong generalization across hospitals and scanners
Prospective Silent Trial	Novel primary samples	0.890	Confirms real-world clinical utility and robust transferability

Table 2: Multi-Fidelity Data Challenges in Materials Foundation Models [10]

Data Fidelity Level	Typical Formation Energy MAE	Key Advantages	Key Limitations for Transferability
GGA/GGA+U (Low)	~194 meV/atom [10]	Computational efficiency, large dataset availability	Limited transferability across bonding environments; noisy data from empirical corrections
r2SCAN (Meta-GGA, High)	~84 meV/atom [10]	Higher general accuracy for strongly bound compounds	High computational cost, limited data scale, energy scale shifts hinder transfer from GGA

Table 3: Comparison of Learning Techniques for Few-Shot Adaptation [12]

Learning Technique	Within-Distribution Performance	Out-of-Distribution Performance	Key Characteristic
Fine-Tuning	Good	Better	Learns more diverse and discriminative features
MAML	Better	Good	Specializes for fast adaptation on similar data distributions
Reptile	Better	Good	Similar to MAML; specializes for the training distribution

Experimental Protocols for Managing the Trade-off

Protocol: Cross-Functional Transfer Learning for Materials FMs

This protocol outlines a method to bridge a pre-trained GGA model to a high-fidelity r2SCAN dataset, addressing the energy shift challenge [10].

1. Pre-Trained Model and Target Dataset Acquisition:

• Research Reagent: A foundation model pre-trained on a large-scale GGA/GGA+U dataset (e.g., CHGNet, M3GNet).
• Research Reagent: A high-fidelity target dataset (e.g., MP-r2SCAN) with consistent labels (energy, forces, stresses).
• Procedure: Acquire the pre-trained model weights and the target dataset. Perform a correlation analysis between the GGA and high-fidelity labels (e.g., r2SCAN energies) to quantify the distribution shift.

2. Elemental Energy Referencing:

• Research Reagent: Isolated elemental reference energies calculated at both the low-fidelity (GGA) and high-fidelity (r2SCAN) levels of theory.
• Procedure: Calculate the systematic energy shift per chemical element between the two fidelity levels. Apply this per-element shift to the pre-trained model's output or the target labels to align the energy scales before fine-tuning.

3. Model Fine-Tuning:

• Research Reagent: High-performance computing cluster with GPU acceleration.
• Procedure: Initialize the model with pre-trained weights. Freeze a significant portion of the initial layers to preserve general features. Fine-tune the later, more task-specific layers on the energy-referenced high-fidelity dataset using a low learning rate and a suitable loss function (e.g., Mean Absolute Error for energies).

4. Validation and Analysis:

• Procedure: Validate the fine-tuned model on a held-out test set from the high-fidelity data. Perform an out-of-distribution test on material systems not present in the fine-tuning set to evaluate retained transferability. Analyze the scaling law to confirm data efficiency gains from transfer learning.

Protocol: Fine-Tuning a Pathology FM for Clinical Biomarker Detection

This protocol details the development of EAGLE, a computational biomarker for EGFR mutation detection in lung cancer, demonstrating a successful real-world application that balances accuracy and transferability [11].

1. Foundation Model and Dataset Curation:

• Research Reagent: An open-source pathology foundation model (e.g., pre-trained on a large corpus of whole slide images).
• Research Reagent: A large, international cohort of digitized H&E-stained lung adenocarcinoma slides (N=8,461 slides from 5 institutions), with corresponding ground truth EGFR mutation status from genomic sequencing.
• Procedure: Curate the dataset, ensuring slides are from multiple institutions and scanned with different scanners to build in diversity. Split data into training (e.g., 5,174 slides), internal validation (e.g., 1,742 slides), and multiple external test sets.

2. Weakly-Supervised Fine-Tuning:

• Research Reagent: High-performance computing environment with substantial GPU memory for processing whole slide images.
• Procedure: Employ a multiple-instance learning framework. Divide whole slide images into smaller tiles. Fine-tune the foundation model using a weakly supervised approach, where only the slide-level label (EGFR mutant/wild-type) is required, eliminating the need for manual, pixel-level annotations.

3. Multi-Cohort Validation:

• Procedure: Evaluate the fine-tuned model on the internal validation set and multiple external test cohorts from different hospitals and geographic locations. Calculate the Area Under the Curve to assess accuracy.

4. Prospective Silent Trial:

• Procedure: Deploy the validated model in a real-time, prospective setting on new, consecutive patient samples. Run the model "silently" without impacting clinical decision-making to confirm its performance and transferability in a true real-world workflow. Analyze clinical utility, such as the potential reduction in rapid molecular tests.

The Scientist's Toolkit: Research Reagent Solutions

Table 4: Essential Research Reagents for Fine-Tuning Foundation Models

Research Reagent / Solution	Function & Application	Examples & Key Features
Pre-Trained Foundation Models	Provides a base of generalizable features for transfer learning, drastically reducing data and compute needs for new tasks.	- CHGNet/M3GNet: For atomistic simulations of materials [10].- Evo 2: For multi-scale biological sequence analysis and design [13].- Pathology FMs: Pre-trained on vast slide libraries for computational pathology [11].
High-Fidelity Target Datasets	Serves as the ground truth for fine-tuning, enabling the model to achieve higher accuracy on a specific task.	- MP-r2SCAN: High-fidelity quantum mechanical data for materials [10].- Multi-institutional Biobanks: Clinically annotated medical images with genomic data [11].
Specialized Software Platforms	Optimizes the training, fine-tuning, and deployment of large foundation models, particularly on biological and chemical data.	- NVIDIA BioNeMo: Offers optimized performance for biological and chemical model training/inference [13].
Elemental Reference Data	Critical for aligning energy scales in multi-fidelity learning for materials science, mitigating negative transfer.	- Isolated Elemental Energies: Calculated at both low- and high-fidelity levels of theory (e.g., GGA and r2SCAN) [10].

The accuracy-transferability trade-off is an inherent property of foundation models, but it is not insurmountable. The protocols and data presented demonstrate that strategic fine-tuning—informed by domain knowledge and robust validation—can yield models that excel in both high-accuracy tasks and out-of-distribution generalization. Key to this success is the use of techniques like elemental energy referencing for materials FMs and multi-institutional, weakly-supervised fine-tuning for medical FMs. Future research should focus on improving multi-fidelity learning algorithms, developing more standardized and expansive benchmarking datasets, and creating more flexible model architectures that can dynamically adapt to data from different distributions. By systematically addressing this core challenge, foundation models will fully realize their potential as transformative tools in scientific discovery and industrial application.

Foundation models for materials science, pre-trained on extensive datasets encompassing diverse chemical spaces, have emerged as powerful tools for initial atomistic simulations [3] [8]. However, their generalist nature often comes at the cost of precision, and they can lack the chemical accuracy required to reliably predict critical system-specific properties such as reaction barriers, phase transition dynamics, and material stability [3] [14]. Fine-tuning has established itself as the pivotal paradigm for bridging this accuracy gap. This process adapts a broad foundation model to a specific chemical system or property, achieving quantitative, often near-ab initio, accuracy while maintaining computational efficiency and requiring significantly less data than training a model from scratch [14].

Recent systematic benchmarks demonstrate that fine-tuning is a universal strategy that transcends model architecture. Evaluations across five leading frameworks—MACE, GRACE, SevenNet, MatterSim, and ORB—reveal consistent and dramatic improvements after fine-tuning on specialized datasets [14]. The adaptation process effectively unifies the performance of these diverse architectures, enabling them to accurately reproduce system-specific physical properties that foundation models alone fail to capture [14].

Quantitative Efficacy of Fine-Tuning

The transformative impact of fine-tuning is quantitatively demonstrated across multiple studies and model architectures. The following table summarizes key performance metrics reported in recent literature.

Table 1: Quantitative Performance Gains from Fine-Tuning Foundation Models

Model / Framework	System Studied	Key Performance Improvement	Data Efficiency
MACE-freeze (f4) [3]	H₂ dissociation on Cu surfaces	Achieved accuracy of from-scratch model with only 20% of training data (664 vs. 3376 configs) [3]	High
Multi-Architecture Benchmark [14]	7 diverse chemical systems	Force errors reduced by 5-15x; Energy errors improved by 2-4 orders of magnitude [14]	High
MACE-MP Foundation Model [3]	Tertiary alloys & surface chemistry	Fine-tuned model with hundreds of datapoints matched accuracy of from-scratch model trained with thousands [3]	High
CHGNet Fine-Tuning [3]	Not Specified	Required >196,000 structures for fine-tuning, similar to from-scratch data needs [3]	Low

The data unequivocally shows that fine-tuning is not merely an incremental improvement but a essential step for achieving quantitative accuracy. The data efficiency is particularly noteworthy, as fine-tuning can reduce the required system-specific data by an order of magnitude or more [3] [14]. This translates directly into reduced computational cost for generating training data via expensive ab initio calculations.

Fine-Tuning Methodologies and Protocols

Several sophisticated fine-tuning methodologies have been developed to optimize performance and mitigate issues like catastrophic forgetting.

Frozen Transfer Learning

This technique involves keeping the parameters of specific layers in the foundation model fixed during further training. By freezing the early layers that capture general chemical concepts (e.g., atomic embeddings), and only updating the later, more task-specific layers (e.g., readout functions), the model retains its broad knowledge while adapting to new data [3].

Experimental Protocol: Frozen Transfer Learning with MACE (MACE-freeze) [3]

• Foundation Model Selection: Begin with a pre-trained MACE-MP model (e.g., "small," "medium," or "large").
• Layer Freezing Strategy:
  • Implement the mace-freeze patch to the MACE software suite.
  • The recommended configuration is MACE-MP-f4, which freezes the initial layers and allows parameters in the product layer and interaction layers to update. This has been shown to peak predictive performance [3].
• Training Configuration:
  • Use the standard mace_run_train script.
  • Set --foundation_model="small" (or path to model).
  • Configure loss weights for energy and forces (e.g., --energy_weight=1.0 --forces_weight=1.0).
  • Utilize an adaptive learning rate scheduler, starting with a relatively low learning rate (e.g., --lr=0.01).
• Validation: Perform k-fold cross-validation and run validation Molecular Dynamics (MD) simulations to ensure stability and accuracy [3].

Multihead Replay Fine-Tuning

This protocol is designed to prevent catastrophic forgetting—where the model loses performance on its original training domain—by concurrently training on the new, specialized dataset and a subset of the original foundation model's training data [15].

Experimental Protocol: Multihead Replay Fine-Tuning [15]

• Foundation Model & Data Preparation:
  • Select a foundation model (e.g., --foundation_model="small").
  • Prepare your system-specific training data (e.g., train.xyz).
  • Have a subset of the original pre-training data (e.g., from Materials Project trajectory data) available for replay.
• Training Execution:
  • Use the mace_run_train script with the argument --multiheads_finetuning=True.
  • Specify the training files and standard hyperparameters. The framework automatically manages the multihead training process.
  • This method is the recommended protocol for fine-tuning Materials Project foundation models as it typically produces more robust and stable models [15].

Integrated Workflow and Visualization

The typical workflow for fine-tuning a materials foundation model, from data generation to deployment of a surrogate potential, is illustrated below. This integrated pipeline ensures both data and computational efficiency.

Diagram 1: Integrated fine-tuning workflow for MLIPs

This workflow highlights the iterative and efficient nature of the process. A key advantage is the optional final step where the fine-tuned foundation model can be used as a reliable, high-accuracy reference to generate labels for training an even more computationally efficient surrogate model, such as one based on the Atomic Cluster Expansion (ACE) [3]. This creates a powerful pipeline for large-scale or massively parallel simulations.

Table 2: Key Resources for Fine-Tuning Materials Foundation Models

Resource Name	Type	Function & Application	Reference/Availability
MACE-MP Foundation Models	Pre-trained Model	Robust, equivariant potential; a common starting point for fine-tuning on diverse materials systems.	[3] [15]
MatterTune Platform	Software Framework	Integrated, user-friendly platform for fine-tuning various FMs (ORB, JMP, MACE); lowers adoption barrier.	[2]
aMACEing Toolkit	Software Toolkit	Unified interface for fine-tuning workflows across multiple MLIP frameworks (MACE, GRACE, etc.).	[14]
Materials Project (MPtrj)	Dataset	A primary source of pre-training data; also used in multihead replay to prevent catastrophic forgetting.	[3] [14]
Multihead Replay Protocol	Training Algorithm	Mitigates catastrophic forgetting during fine-tuning by replaying original training data; recommended for MACE-MP.	[15]
Frozen Transfer Learning	Training Algorithm	Enhances data efficiency by freezing general-purpose layers and updating only task-specific layers.	[3]

Fine-tuning has firmly established itself as a non-negotiable paradigm for unlocking the full potential of materials foundation models. The quantitative evidence is clear: this process transforms robust but general-purpose potentials into highly accurate, system-specific tools capable of predicting challenging properties like reaction barriers and phase behavior [3] [14]. By leveraging strategies such as frozen transfer learning and multihead replay, researchers can achieve this precision with remarkable data efficiency, overcoming a critical bottleneck in computational materials science. As unified toolkits and platforms like MatterTune and the aMACEing Toolkit continue to emerge, these advanced methodologies are becoming increasingly accessible, paving the way for their widespread adoption in accelerating materials discovery and drug development.

The integration of atomistic foundation models (FMs) is revolutionizing biomedical research by enabling highly accurate and data-efficient simulations of complex biological systems. These models, pre-trained on vast and diverse datasets, provide a robust starting point for understanding intricate biomedical phenomena. Fine-tuning strategies, such as frozen transfer learning, allow researchers to adapt these powerful models to specific downstream tasks with limited data, overcoming a significant bottleneck in computational biology and materials science [3] [2]. This article details key applications and provides standardized protocols for employing fine-tuned FMs in two critical areas: predicting protein-ligand interactions for drug discovery and designing stable, functional biomaterials.

Fine-Tuning Foundations: Core Concepts and Strategies

Foundation models for atomistic systems are typically graph neural networks (GNNs) trained on large-scale datasets like the Materials Project to predict energies, forces, and stresses from atomic structures [2]. Their strength lies in learning general, transferable representations of atomic interactions.

• Frozen Transfer Learning: This is a highly data-efficient fine-tuning method where the initial layers of a pre-trained FM are kept fixed ("frozen"), and only the later layers are updated on the new, task-specific dataset. This approach preserves the general knowledge acquired during pre-training while efficiently adapting the model to a specialized domain, achieving high accuracy with hundreds, rather than thousands, of data points [3].
• Platforms for Implementation: Frameworks like MatterTune have been developed to lower the barrier for researchers. MatterTune integrates state-of-the-art FMs (e.g., MACE, MatterSim, ORB) and provides a user-friendly interface for flexible fine-tuning and application across diverse materials informatics tasks [2].

The following workflow illustrates the typical process for fine-tuning a foundation model for a specialized biomedical application:

Fine-Tuning Workflow for Biomedical Applications

Application Note 1: Predicting Protein-Ligand Binding Dynamics

Objective: To accurately identify dynamic binding "hotspots" and predict ligand poses and affinities by integrating molecular dynamics (MD) with insights from fine-tuned FMs, thereby accelerating target and drug discovery [16] [17].

Quantitative Binding Dynamics

A large-scale analysis of 100 protein-ligand complexes provided key quantitative metrics that define stable binding interactions. These parameters are crucial for validating both MD and docking predictions [16].

Table 1: Key Quantitative Parameters for Protein-Ligand Binding Sites from MD Simulations [16]

Parameter	Description	Median Value (Interquartile Range)
Binding Residue Backbone RMSD	Measures structural fluctuation of binding site residues.	1.2 Å (0.8 Å)
Ligand RMSD	Measures stability of the bound ligand pose.	1.6 Å (1.0 Å)
Minimum SASA of Binding Residues	Minimum solvent-accessible surface area of binding residues.	2.68 Ų (0.43 Ų)
Maximum SASA of Binding Residues	Maximum solvent-accessible surface area of binding residues.	3.2 Ų (0.59 Ų)
High-Occupancy H-Bonds	Hydrogen bonds with persistence >71 ns during a 100 ns MD simulation.	86.5% of all H-bonds

Experimental Protocol: MD Simulation for Hotspot Validation

Methodology: This protocol uses classical Molecular Dynamics (cMD) to validate the stability of a protein-ligand complex and identify dynamic hotspots, based on the workflow established by International Journal of Molecular Sciences [16].

• System Preparation:

  • Obtain a high-resolution co-crystal structure of the protein-ligand complex from the RCSB Protein Data Bank. Prefer structures with a resolution of <2.0 Å and without mutations in the binding pocket.
  • Parameterize the ligand using standard force fields (e.g., GAFF2) with tools like acpype or the RESP charge method.
  • Solvate the complex in a triclinic water box (e.g., TIP3P water model) with a minimum 1.0 nm distance between the protein and box edge.
  • Add ions (e.g., Na⁺, Cl⁻) to neutralize the system's charge and simulate a physiological salt concentration (e.g., 0.15 M NaCl).

• Simulation Setup:

  • Use a molecular dynamics package such as GROMACS.
  • Apply periodic boundary conditions.
  • Employ a force field like AMBER99SB-ILDN or CHARMM36 for the protein.
  • Set long-range electrostatics treatment using the Particle Mesh Ewald (PME) method.

• Energy Minimization and Equilibration:

  • Energy Minimization: Run steepest descent minimization until the maximum force is below 1000 kJ/mol·nm.
  • NVT Equilibration: Equilibrate the system for 100 ps in the canonical ensemble (constant Number of particles, Volume, and Temperature) using a thermostat (e.g., V-rescale) at 300 K.
  • NPT Equilibration: Equilibrate the system for 100 ps in the isothermal-isobaric ensemble (constant Number of particles, Pressure, and Temperature) using a barostat (e.g., Berendsen) at 1 bar.

• Production MD Run:

  • Run a production simulation for a minimum of 100 ns. Save atomic coordinates every 10 ps for analysis.

• Data Analysis:

  • Root Mean Square Deviation (RMSD): Calculate the backbone RMSD for the entire protein, the binding site residues, and the ligand to assess stability.
  • Hydrogen Bond Occupancy: Use a tool like gmx hbond in GROMACS to compute the existence matrix of H-bonds between binding residues and the ligand. Classify occupancy as low (0-30 ns), moderate (31-70 ns), or high (71-100 ns).
  • Solvent-Accessible Surface Area (SASA): Calculate the SASA for binding site residues to understand their exposure and interaction with the solvent and ligand.

The Scientist's Toolkit: Protein-Ligand Interaction Analysis

Table 2: Essential Research Reagents and Software for Protein-Ligand Studies

Item	Function/Description	Example Use Case
GROMACS	A versatile package for performing MD simulations.	Used for the energy minimization, equilibration, and production MD runs in the protocol above [16].
HAMD	An alternative MD engine for simulating biomolecular systems.	Can be used for simulating large complexes or specific force fields.
Force Fields (AMBER, CHARMM)	Parameter sets defining potential energy functions for atoms.	Provides the physical rules for atomic interactions during the simulation (e.g., AMBER99SB-ILDN) [16].
GAFF2 (Generalized Amber Force Field 2)	A force field for small organic molecules.	Used for parameterizing drug-like ligands in the protein-ligand system [16].
PyMOL / VMD	Molecular visualization systems.	Used for visualizing the initial structure, simulation trajectories, and interaction analysis (e.g., H-bond plotting) [16].
High-Resolution Co-crystal Structure	A experimentally determined structure of the protein-ligand complex.	Serves as the essential starting point and ground truth for the simulation [16].

Application Note 2: Engineering Biomaterials with Targeted Stability

Objective: To design hydrogel-based bioinks and enzyme-responsive biomaterials with optimal printability, long-term mechanical stability, and tailored biocompatibility for applications in regenerative medicine and drug delivery [18] [19].

Key Parameters for Biomaterial Stability and Function

The design of functional biomaterials requires balancing multiple properties. Rheological properties dictate printability, while cross-linking and enzymatic sensitivity determine in-vivo performance and stability.

Table 3: Critical Parameters for Hydrogel-Based Biomaterial Design [18] [19]

Parameter	Influence on Function	Target/Example Value
Storage Modulus (G′)	Determines the mechanical stiffness and elastic solid-like behavior of the scaffold.	Should be > Loss Modulus (G″) for shape retention post-printing [18].
Shear-Thinning Behavior	Enables extrusion during bioprinting by reducing viscosity under shear stress.	Essential property for extrusion-based bioprinting [18].
Enzyme-Responsive Peptide Linker	Confers specific, on-demand degradation or drug release in target tissues.	MMP-2/9 cleavable sequence (e.g., PLGLAG) for targeting inflamed or remodeling tissues [19].
Dual Cross-Linking	Enhances long-term mechanical stability and integrity of the printed construct.	Combination of ionic (e.g., CaCl₂ for alginate) and photo-crosslinking (e.g., UV for GelMA) [18].
Swelling Ratio	Affects the scaffold's pore size, permeability, and mechanical load bearing.	Must be tuned to match the target tissue environment.

Experimental Protocol: Rheology and Printability Assessment of Bioinks

Methodology: This protocol outlines a sequence of rheological tests to quantitatively correlate a bioink's properties with its printability and stability, as detailed in the Journal of Materials Chemistry B [18].

• Bioink Formulation:

  • Prepare the bioink formulation. An example optimal formulation is 4% Alginate (Alg), 10% Carboxymethyl Cellulose (CMC), and 8-16% Gelatin Methacrylate (GelMA) [18].
  • Ensure all components are fully dissolved and homogeneously mixed.

• Rheological Characterization: (Perform using a rotational rheometer with a parallel plate geometry)

  • Flow Sweep Test: To assess shear-thinning.
    • Set the temperature to the printing temperature (e.g., 20-25°C).
    • Apply a linearly increasing shear rate (e.g., from 0.1 to 100 s⁻¹).
    • Record the viscosity. A significant decrease in viscosity with increasing shear rate confirms shear-thinning behavior.
  • Amplitude Sweep Test: To determine the linear viscoelastic region (LVE) and yield stress.
    • At a fixed frequency (e.g., 10 rad/s), apply an oscillatory shear strain from 0.1% to 1000%.
    • Record the storage (G′) and loss (G″) moduli.
    • The yield stress is identified as the point where G′ drops sharply, indicating the transition from elastic to viscous behavior.
  • Thixotropy Test: To evaluate self-healing and structural recovery.
    • Apply a low oscillatory shear (e.g., 1% strain, within the LVE) for 2 minutes to simulate post-printing recovery.
    • Switch to a high oscillatory shear (e.g., 200% strain) for 1 minute to simulate the extrusion process.
    • Immediately return to the low shear for 5 minutes and monitor the recovery of G′.
  • Time Sweep Test: To measure curing kinetics and final stiffness.
    • After depositing the bioink, initiate cross-linking (e.g., expose to UV light for GelMA, or apply CaCl₂ spray for alginate).
    • Monitor G′ and G″ over time at a fixed strain and frequency until the moduli plateau.

• Printability Assessment:

  • Use an extrusion-based 3D bioprinter.
  • Print a standard test structure (e.g., a grid or filament) at the predetermined pressure and nozzle speed.
  • Quantify printability using metrics like filament diameter consistency, ability to form freestanding filaments, and the printability value (Pr).

The Scientist's Toolkit: Biomaterial Formulation and Testing

Table 4: Essential Reagents and Equipment for Biomaterial Development

Item	Function/Description	Example Use Case
Alginate (Alg)	A natural polymer that forms ionic hydrogels with divalent cations.	Provides the primary scaffold structure and enables ionic cross-linking with CaCl₂ [18].
Gelatin Methacrylate (GelMA)	A photopolymerizable bioink component derived from gelatin.	Provides cell-adhesive motifs (RGD) and enables UV-triggered covalent cross-linking for stability [18].
Carboxymethyl Cellulose (CMC)	A viscosity-modifying polymer.	Enhances the rheological properties and printability of the bioink formulation [18].
Photoinitiator (e.g., LAP)	A compound that generates radicals upon UV light exposure.	Used to initiate the cross-linking of GelMA in the bioink [18].
Rotational Rheometer	An instrument for measuring viscoelastic properties.	Used to perform flow sweeps, amplitude sweeps, and thixotropy tests to characterize the bioink [18].
MMP-Cleavable Peptide (PLGLAG)	A peptide sequence degraded by Matrix Metalloproteinases.	Incorporated into hydrogels as a cross-linker for targeted, enzyme-responsive drug release in diseased tissues [19].

The following diagram illustrates the decision-making process and key considerations in the biomaterial design pipeline, from formulation to functional assessment:

Biomaterial Design and Evaluation Pipeline

A Practical Toolkit: Fine-Tuning Methods and Platforms

The field of materials science is undergoing a significant transformation driven by the development of deep learning-based interatomic potentials. These models, often termed atomistic foundation models, leverage large-scale pre-training on diverse datasets to achieve broad applicability across the periodic table [2]. They represent a paradigm shift from traditional, narrowly focused machine-learned potentials towards general-purpose, universal interatomic potentials that can be fine-tuned for specific applications with remarkable data efficiency [3] [2]. Among the most prominent models in this rapidly evolving landscape are MACE, MatterSim, ORB, and GRACE. These models share the common objective of accurately simulating atomic interactions to predict material properties and behaviors, yet they differ in their architectural approaches, training methodologies, and specific strengths. This overview provides a detailed comparison of these leading models, focusing on their technical specifications, performance benchmarks, and practical implementation protocols for materials research and discovery.

Model Specifications and Comparative Analysis

The following sections detail the core architectures, training approaches, and performance characteristics of each model, with quantitative comparisons summarized in subsequent tables.

MACE (Multi-Atomic Cluster Expansion)

MACE employs an architecture that incorporates many-body messages and equivariant features, which effectively capture the symmetry properties of atomic structures [3]. This design enables high accuracy in modeling complex atomic environments. The model has been trained on the Materials Project dataset (MPtrj) and has demonstrated impressive performance across various benchmark systems [3]. A key advantage of the MACE framework is its suitability for fine-tuning strategies. Research has shown that applying transfer learning with partially frozen weights and biases—where parameters in earlier layers are fixed while later layers are adapted to new tasks—significantly enhances data efficiency [3]. This approach, implemented through the mace-freeze patch, allows MACE models to reach chemical accuracy with only hundreds of datapoints instead of the thousands typically required for training from scratch [3].

MatterSim

Developed by Microsoft Research, MatterSim is designed for simulating materials across wide ranges of temperature (0–5000 K) and pressure (0–1000 GPa) [20]. It utilizes a deep graph neural network trained through an active learning approach where a first-principles supervisor guides the exploration of materials space [20]. MatterSim demonstrates a ten-fold improvement in precision compared to prior models, with a mean absolute error of 36 meV/atom on its comprehensive MPF-TP dataset [20]. The model is particularly noted for its ability to predict Gibbs free energies with near-first-principles accuracy, enabling computational prediction of experimental phase diagrams [20]. MatterSim also serves as a platform for continuous learning, achieving up to 97% reduction in data requirements when fine-tuned for specific applications [20]. Two pre-trained versions are available: MatterSim-v1.0.0-1M (faster) and MatterSim-v1.0.0-5M (more accurate) [21] [22].

ORB

ORB represents a fast, scalable neural network potential that prioritizes computational efficiency without sacrificing accuracy [23]. Its architecture is based on a Graph Network Simulator augmented with smoothed graph attention mechanisms, where messages between nodes are updated based on both attention weights and distance-based cutoff functions [23]. A distinctive feature of ORB is that it learns atomic interactions and their invariances directly from data rather than relying on architecturally constrained models with built-in symmetries [23]. Upon release, ORB achieved a 31% reduction in error over other methods on the Matbench Discovery benchmark while being 3-6 times faster than existing universal potentials across various hardware platforms [23]. The model is available under the Apache 2.0 license, permitting both research and commercial use [23].

GRACE

It is important to note a significant naming ambiguity in the literature. While the search results reveal several models named GRACE, the most relevant in the context of materials foundation models is briefly mentioned in a review as an example of models trained on diverse chemical structures [3]. However, detailed technical specifications for a materials-focused GRACE model are not available in the search results, which instead predominantly refer to clinical and medical models (e.g., GRACE-ICU for patient risk assessment and GRACE score for acute coronary events) [24] [25] [26]. This overview will therefore focus on the well-documented MACE, MatterSim, and ORB models for subsequent comparative analysis and protocols.

Table 1: Core Model Specifications and Training Details

Model	Architecture	Training Data Size	Parameter Count	Training Objective
MACE-MP-0	Many-body messages with equivariant features [3]	1.58M structures [2]	4.69M [2]	Energy, forces, stress [2]
MatterSim-v1	Deep Graph Neural Network [20]	17M structures [2]	4.55M [2]	Energy, forces, stress [2]
ORB-v1	Graph Network Simulator with attention [23]	32.1M structures [2]	25.2M [2]	Denoising + energy, forces, stress [2]
GRACE	Information not available in search results	Information not available in search results	Information not available in search results	Information not available in search results

Table 2: Performance Characteristics and Applications

Model	Key Strengths	Reported Accuracy	Optimal Fine-tuning Strategy
MACE	Data-efficient fine-tuning [3]	Chemical accuracy with 10-20% of data [3]	Frozen transfer learning (MACE-freeze) [3]
MatterSim	Temperature/pressure robustness [20]	36 meV/atom MAE on MPF-TP [20]	Active learning with first-principles supervisor [20]
ORB	Computational speed [23]	31% error reduction on Matbench Discovery [23]	Not specified in search results
GRACE	Information not available	Information not available	Information not available

Fine-tuning Strategies and Experimental Protocols

Frozen Transfer Learning for MACE

Frozen transfer learning has emerged as a particularly effective fine-tuning strategy for foundation models, especially for MACE [3]. This protocol involves freezing specific layers of the pre-trained model during fine-tuning, which preserves general features learned from the original large dataset while adapting the model to specialized tasks with limited data.

Table 3: MACE Frozen Transfer Learning Configuration

Component	Specification	Function
Foundation Model	MACE-MP "small", "medium", or "large" [3]	Provides pre-trained base with broad knowledge
Fine-tuning Data	100-1000 structures [3]	Task-specific data for model adaptation
Frozen Layers	Typically 4 layers (MACE-MP-f4 configuration) [3]	Preserves general features from pre-training
Active Layers	Readout and product layers [3]	Adapts model to specific task
Performance	Similar accuracy with 20% of data vs. from-scratch training with 100% [3]	Enables high accuracy with minimal data

Experimental Protocol for MACE Fine-tuning:

• Model Selection: Choose an appropriate pre-trained MACE-MP model ("small" recommended for balance of accuracy and efficiency) [3]
• Data Preparation: Curate a task-specific dataset of several hundred structures with corresponding energies and forces
• Model Configuration: Apply the mace-freeze patch to freeze the first four layers of the network [3]
• Training: Fine-tune only the unfrozen layers using the specialized dataset
• Validation: Validate against benchmark systems to ensure maintained accuracy on original capabilities while achieving improved performance on target domain

Active Learning Protocol for MatterSim

MatterSim employs an active learning workflow that integrates a deep graph neural network with a materials explorer and first-principles supervisor [20]. This approach continuously improves the model by targeting the most uncertain regions of the materials space.

MatterSim Active Learning Workflow

Implementation Steps:

• Initialization: Begin with a curated dataset from existing sources [20]
• Model Training: Train the MatterSim model on the current dataset
• Uncertainty Sampling: Use the trained model as a surrogate to identify regions of materials space with high prediction uncertainty [20]
• Targeted Exploration: Deploy materials explorers to gather additional structures from these uncertain regions [20]
• First-Principles Validation: Label the newly sampled structures using first-principles calculations (typically DFT at PBE level with Hubbard U correction) [20]
• Iterative Enrichment: Add the newly labeled structures to the training set and repeat the process through multiple active learning cycles [20]

This protocol enables MatterSim to achieve comprehensive coverage of materials space beyond the limitations of static databases, which often contain structural biases toward highly symmetric configurations near local energy minima [20].

Implementation Framework: MatterTune

The MatterTune framework provides an integrated, user-friendly platform for fine-tuning atomistic foundation models, including support for ORB, MatterSim, and MACE [2]. This platform addresses the current limitation in software infrastructure for leveraging atomistic foundation models across diverse materials informatics tasks.

Table 4: MatterTune Framework Components

Subsystem	Function	Supported Capabilities
Model Subsystem	Manages different model architectures	Supports JMP, ORB, MatterSim, MACE, EquiformerV2 [2]
Data Subsystem	Handles diverse input formats	Standardized structure representation based on ASE package [2]
Trainer Subsystem	Controls fine-tuning procedures	Customizable training loops with distributed training support [2]
Application Subsystem	Enables downstream tasks	Property prediction, molecular dynamics, materials screening [2]

Key Advantages of MatterTune:

• Modular Design: Decouples models, data, algorithms, and applications for high adaptability [2]
• Standardized Abstractions: Provides unified interfaces for diverse model architectures [2]
• Customizable Fine-tuning: Supports various fine-tuning strategies beyond black-box approaches [2]
• Broad Application Support: Enables use of foundation models for tasks beyond force fields, including property prediction and materials discovery [2]

The Scientist's Toolkit: Essential Research Reagents

Table 5: Key Research Reagents and Computational Resources

Resource	Type	Function	Availability
MatterTune	Software framework	Fine-tuning atomistic foundation models [2]	GitHub: Fung-Lab/MatterTune [2]
MACE-Freeze	Software patch	Implements frozen transfer learning for MACE [3]	Integrated in MACE software suite [3]
Materials Project	Database	Source of training structures and references [3]	materialsproject.org
ASE (Atomic Simulation Environment)	Software library	Structure manipulation and analysis [2]	wiki.fysik.dtu.dk/ase
MPtrj Dataset	Training data	Materials Project trajectory data for foundation models [3]	materialsproject.org

The development of universal interatomic potentials represents a transformative advancement in computational materials science. MACE, MatterSim, and ORB each offer distinct approaches to addressing the challenge of accurate, efficient atomistic simulations across diverse chemical spaces and thermodynamic conditions. While these models demonstrate impressive zero-shot capabilities, their true potential is realized through strategic fine-tuning approaches such as frozen transfer learning and active learning, which enable researchers to achieve high accuracy on specialized tasks with minimal data requirements. Frameworks like MatterTune further lower barriers to adoption by providing standardized interfaces and workflows for leveraging these powerful models across diverse materials informatics applications. As these foundation models continue to evolve, they are poised to dramatically accelerate materials discovery and design through accurate, efficient prediction of structure-property relationships across virtually the entire periodic table.

Frozen transfer learning has emerged as a pivotal technique for enhancing the data efficiency of foundation models in atomistic materials research. This method involves taking a pre-trained model on a large, diverse dataset and fine-tuning it for a specific task by keeping (freezing) the parameters in a subset of its layers while updating (unfreezing) others. Foundation models, pre-trained on extensive datasets, learn robust, general-purpose representations of atomic interactions. However, they often lack the specialized accuracy required for predicting precise properties like reaction barriers or phase transitions in specific systems. Frozen transfer learning addresses this by leveraging the model's general knowledge while efficiently adapting it to specialized tasks with minimal data, thereby preventing overfitting and the phenomenon of "catastrophic forgetting" where a model loses previously learned information [3].

In the domain of materials science and drug development, where generating high-quality training data from first-principles calculations is computationally prohibitive, this approach is particularly valuable. It represents a paradigm shift from building task-specific models from scratch to adapting versatile, general models, making high-accuracy machine-learned interatomic potentials accessible for a wider range of scientific investigations [3] [2].

Quantitative Analysis of Data Efficiency

The application of frozen transfer learning to materials foundation models demonstrates significant gains in data efficiency and predictive performance across different systems.

Table 1: Performance Comparison of Fine-Tuning Strategies on the H₂/Cu System [3]

Model Type	Training Data Used	Energy RMSE (meV/atom)	Force RMSE (meV/Å)	Primary Benefit
From-Scratch MACE	100% (~3,376 configs)	~3.0	~90	Baseline accuracy
MACE-MP-f4 (Frozen)	20% (~664 configs)	~3.0	~90	Similar accuracy with 80% less data
MACE-MP-f4 (Frozen)	10% (~332 configs)	~5.5	~125	Good accuracy with 90% less data

Table 2: Impact of Foundation Model Size on Fine-Tuning Efficiency [3]

Foundation Model	Number of Parameters	Relative Fine-Tuning Compute	Final Accuracy on H₂/Cu
MACE-MP "Small"	~4.69 million	1.0x (Baseline)	High
MACE-MP "Medium"	~9.06 million	~1.8x	Comparable to Small
MACE-MP "Large"	~16.2 million	~3.5x	Comparable to Small

Studies on reactive hydrogen chemistry on copper surfaces (H₂/Cu) show that a frozen transfer-learned model (MACE-MP-f4) achieves accuracy comparable to a model trained from scratch using only 20% of the original training data—hundreds of data points instead of thousands [3]. This strategy also reduces GPU memory consumption by up to 28% compared to full fine-tuning, as freezing layers reduces the number of parameters that need to be stored and updated during training [27]. The "small" foundation model is often sufficient for fine-tuning, offering an optimal balance between performance and computational cost [3].

Experimental Protocols for Frozen Transfer Learning

Protocol 1: Fine-Tuning a Foundation Potential for a Surface Chemistry Application

This protocol details the procedure for adapting a general-purpose MACE-MP foundation model to study the dissociative adsorption of H₂ on Cu surfaces [3].

• Objective: To create a highly accurate and data-efficient machine learning interatomic potential for H₂/Cu reactive dynamics.
• Primary Materials & Models:
  • Foundation Model: Pre-trained MACE-MP "small" model [3] [2].
  • Target Data: A dataset of ~4,230 structures with energies and forces for H₂/Cu systems, derived from first-principles calculations and active learning [3].
  • Software: MACE software suite with the mace-freeze patch [3].

Step-by-Step Procedure:

• Data Preparation and Partitioning:

  • Curate your target dataset (e.g., H₂/Cu structures with DFT-calculated energies and forces).
  • Split the data into training (e.g., 80%), validation (e.g., 10%), and test sets (e.g., 10%). For data efficiency analysis, create smaller subsets (e.g., 10%, 20%) from the full training set.

• Model and Optimizer Setup:

  • Load the pre-trained MACE-MP "small" model.
  • Configure the mace-freeze patch to freeze all layers up to and including the first three interaction layers. This corresponds to the "f4" configuration, which keeps the foundational feature detectors frozen while allowing the later layers to specialize [3].
  • Initialize an optimizer (e.g., Adam) with a reduced learning rate (e.g., 1e-4) for the unfrozen parameters to enable stable adaptation.

• Training and Validation Loop:

  • Train the model on the target training set, using the validation set to monitor for overfitting.
  • Use a loss function that combines energy and force errors.
  • Employ early stopping if the validation loss does not improve for a predetermined number of epochs.

• Model Evaluation:

  • Evaluate the final model on the held-out test set.
  • Report key metrics: Root Mean Square Error (RMSE) on energies and forces, comparing performance against a from-scratch model and the baseline foundation model.

Protocol 2: Fine-Tuning for Ternary Alloy Properties

This protocol outlines the adaptation for predicting the stability and elastic properties of ternary alloys [3].

• Objective: To develop a specialized model for accurate property prediction in complex ternary alloy systems.
• Primary Materials & Models:
  • Foundation Model: Pre-trained MACE-MP or CHGNet model [3] [2].
  • Target Data: A smaller dataset of ternary alloy structures with associated stability and elastic property labels.

Step-by-Step Procedure:

• Data Preparation:

  • Assemble a dataset of ternary alloy structures with calculated energies, forces, and target properties (e.g., elastic tensor components).
  • Perform a train/validation/test split as in Protocol 1.

• Freezing Strategy Selection:

  • For CHGNet, a common strategy is to freeze the entire graph neural network backbone and only fine-tune the readout layers. For MACE, the "f4" or "f5" configuration is recommended [3] [28].
  • This approach preserves the general physical knowledge of atomic interactions while efficiently learning the mapping to the new properties.

• Fine-Tuning Execution:

  • Load the foundation model and apply the chosen freezing configuration.
  • Use a loss function tailored to the target properties (e.g., including a stress term for elastic properties).
  • Proceed with training and validation as in Steps 3 and 4 of Protocol 1.

• Surrogate Model Generation (Optional):

  • Use the fine-tuned, high-accuracy model to generate labels for a larger set of configurations.
  • Train a more computationally efficient surrogate model, such as an Atomic Cluster Expansion (ACE) potential, on this generated data, creating a fast model for large-scale simulations [3].

Workflow and Strategy Visualization

Figure 1: A decision workflow for selecting an optimal layer-freezing strategy, based on dataset characteristics and project goals [3] [27].

Table 3: Key Resources for Frozen Transfer Learning Experiments

Resource Name	Type	Function / Application	Example / Reference
MACE-MP Models	Foundation Model	Pre-trained interatomic potentials providing a robust starting point for fine-tuning.	MACE-MP-0, MACE-MP-1 [3] [2]
mace-freeze Patch	Software Tool	Enables layer-freezing for fine-tuning within the MACE software suite.	[3]
MatterTune	Software Platform	Integrated, user-friendly framework for fine-tuning various atomistic foundation models (ORB, MatterSim, MACE).	[2]
Materials Project (MPtrj)	Pre-training Dataset	Large-scale dataset of DFT calculations used to train foundation models.	~1.58M structures [3] [2]
H₂/Cu Surface Dataset	Target Dataset	Task-specific dataset for benchmarking fine-tuning performance on reactive chemistry.	4,230 structures [3]
Atomic Cluster Expansion (ACE)	Surrogate Model	A fast, efficient potential that can be trained on data generated by a fine-tuned model for large-scale MD.	[3]

Parameter-Efficient Fine-Tuning (PEFT) represents a strategic shift in how researchers adapt large, pre-trained models to specialized tasks. Instead of updating all of a model's parameters—a computationally expensive process known as full fine-tuning—PEFT methods selectively modify a small portion of the model or add lightweight, trainable components. This drastically reduces computational requirements, memory consumption, and storage overhead without significantly compromising performance [29]. In natural language processing (NLP) and computer vision, techniques like Low-Rank Adaptation (LoRA) have become standard practice. However, the application of PEFT to molecular systems presents unique challenges, primarily due to the critical need to preserve fundamental physical symmetries—a requirement that conventional methods often violate [30] [31].

The emergence of atomistic foundation models pre-trained on vast quantum chemical datasets has created an urgent need for efficient adaptation strategies. These models learn general, transferable representations of atomic interactions but often require specialization to achieve chemical accuracy on specific systems, such as novel materials or complex biomolecular environments [3] [32]. This application note details the theoretical foundations, practical protocols, and recent advancements in PEFT for molecular systems, with a focused examination of LoRA and its equivariant extension, ELoRA, providing researchers with a framework for efficient and physically consistent model specialization.

Fundamental Techniques: From LoRA to ELoRA

Standard LoRA and Its Limitations in Scientific Domains

Low-Rank Adaptation (LoRA) is a foundational PEFT technique that operates on a core hypothesis: the weight updates (ΔW) required to adapt a pre-trained model to a new task have a low "intrinsic rank." Instead of computing the full ΔW matrix, LoRA directly learns a decomposed representation through two smaller, trainable matrices, A and B, such that ΔW = AB [29] [33]. During training, only A and B are updated, while the original pre-trained weights W remain frozen. The updated forward pass for a layer therefore becomes: h = Wx + BAx, where r (the rank) is a key hyperparameter, typically much smaller than the original matrix dimensions [33].

This approach offers significant advantages:

• Memory Efficiency: It dramatically reduces the number of trainable parameters, often to less than 1% of the original model, enabling fine-tuning on consumer-grade GPUs [29].
• Modularity: Multiple, task-specific LoRA adapters can be trained independently and swapped on top of a single, frozen base model [34].

However, a critical limitation arises when applying standard LoRA to geometric models like Equivariant Graph Neural Networks (GNNs). The arbitrary matrices A and B do not respect the rotational, translational, and permutational symmetries (SO(3) equivariance) that are fundamental to physical systems. Mixing different tensor orders during the adaptation process inevitably breaks this equivariance, leading to physically inconsistent predictions [30] [35].

ELoRA: Preserving Equivariance for Molecular Systems

ELoRA (Equivariant Low-Rank Adaptation) was introduced to address the symmetry-breaking shortfall of standard LoRA. Designed specifically for SO(3) equivariant GNNs, which serve as the backbone for many pre-trained interatomic potentials, ELoRA ensures that fine-tuning preserves equivariance—a critical property for physical consistency [30] [31].

The key innovation of ELoRA is its path-dependent decomposition for weight updates. Unlike standard LoRA, which applies the same low-rank update across all feature channels, ELoRA applies separate, independent low-rank adaptations to each irreducible representation (tensor order) path within the equivariant network [35]. This method prevents the mixing of features from different tensor orders, thereby strictly preserving the equivariance property throughout the fine-tuning process [30]. This approach not only maintains physical consistency but also leverages low-rank adaptations to significantly improve data efficiency, making it highly effective even with small, task-specific datasets [31].

Performance Comparison and Quantitative Benchmarks

The effectiveness of ELoRA and related advanced PEFT methods is demonstrated through comprehensive benchmarks on standard molecular datasets. The table below summarizes their performance in predicting energies and forces—key quantities in atomistic simulations.

Table 1: Performance Comparison of Fine-Tuning Methods on Molecular Benchmarks

Method	Key Principle	rMD17 (Organic) Energy MAE	rMD17 (Organic) Force MAE	10 Inorganic Datasets Avg. Energy MAE	10 Inorganic Datasets Avg. Force MAE	Trainable Parameters
Full Fine-Tuning	Updates all model parameters	Baseline	Baseline	Baseline	Baseline	100%
ELoRA [30] [31]	Path-dependent, equivariant low-rank adaptation	25.5% improvement vs. full fine-tuning	23.7% improvement vs. full fine-tuning	12.3% improvement vs. full fine-tuning	14.4% improvement vs. full fine-tuning	Highly Reduced (<5%)
MMEA [35]	Scalar gating modulates feature magnitudes	State-of-the-art levels	State-of-the-art levels	State-of-the-art levels	State-of-the-art levels	Fewer than ELoRA
Frozen Transfer Learning (MACE-MP-f4) [3]	Freezes early layers of foundation model	Similar accuracy to from-scratch training with ~20% of data	Similar accuracy to from-scratch training with ~20% of data	Not Specified	Not Specified	Highly Reduced

A recent advancement beyond ELoRA is the Magnitude-Modulated Equivariant Adapter (MMEA). Building on the insight that a well-trained equivariant backbone already provides robust feature bases, MMEA employs an even lighter strategy. It uses lightweight scalar gates to dynamically modulate feature magnitudes on a per-channel and per-multiplicity basis without mixing them. This approach preserves strict equivariance and has been shown to consistently outperform ELoRA across multiple benchmarks while training fewer parameters, suggesting that in many scenarios, modulating channel magnitudes is sufficient for effective adaptation [35].

Experimental Protocols for Fine-Tuning Molecular Foundation Models

Protocol 1: Fine-Tuning with ELoRA

This protocol outlines the steps for adapting a pre-trained equivariant GNN using the ELoRA method.

Table 2: Research Reagent Solutions for ELoRA Fine-Tuning

Item Name	Function / Description	Example / Specification
Pre-trained Equivariant GNN	The base model providing foundational knowledge of interatomic interactions.	Models: MACE, EquiformerV2, NequIP, eSEN [32] [2].
Target Dataset	The small, task-specific dataset for adaptation.	A few hundred to a few thousand local structures of the target molecular system [35].
ELoRA Adapter Modules	The trainable, path-specific low-rank matrices injected into the base model.	Rank r is a key hyperparameter; code available at [30].
Software Framework	Library providing implementations of equivariant models and PEFT methods.	e3nn framework, MatterTune platform [2].
Optimizer	Algorithm for updating the trainable parameters during fine-tuning.	AdamW or SGD; choice has minimal impact on performance with low ranks [33].

Procedure:

• Model Preparation: Load a pre-trained equivariant foundation model (e.g., MACE-MP) and freeze all its parameters.
• ELoRA Injection: Inject ELoRA adapter modules into the designated layers of the model (typically the linear layers within interaction blocks). These modules are initialized with small, random values.
• Dataset Splitting: Split your target dataset into training (80%), validation (10%), and test (10%) sets. Ensure the dataset contains structures, energies, and forces.
• Loss Function Configuration: Define a composite loss function, L = α * L_energy + β * L_forces, where α and β are scaling factors (e.g., 1 and 100 respectively) to balance the importance of energy and force accuracy.
• Hyperparameter Tuning:
  • Set the rank r of the ELoRA matrices. Start with a low value (e.g., 2, 4, or 8) and increase if performance is inadequate [33].
  • Choose a learning rate for the optimizer, typically in the range of 1e-4 to 1e-3, as only the adapters are being trained.
  • Set the number of epochs based on dataset size, monitoring for overfitting on the validation set.
• Training Loop: For each epoch, iterate through the training set, performing forward and backward passes to update only the ELoRA adapter parameters.
• Validation and Early Stopping: Evaluate the model on the validation set after each epoch. Stop training if validation loss does not improve for a predetermined number of epochs (patience).
• Final Evaluation: Assess the final fine-tuned model's performance on the held-out test set to gauge its generalization capability.

The following workflow diagram illustrates the ELoRA fine-tuning process:

Protocol 2: Frozen Transfer Learning for Data Efficiency

An alternative PEFT strategy, particularly effective with very large foundation models, is frozen transfer learning. This method involves freezing a significant portion of the model's early layers and only fine-tuning the later layers on the new data [3].

Procedure:

• Model Selection: Choose a foundation model pre-trained on a massive, diverse dataset (e.g., MACE-MP, JMP, or a UMA model trained on OMol25) [32] [2].
• Layer Freezing Strategy: Freeze the parameters in the initial layers of the network. For example, with a MACE model, you might freeze all layers up to and including the first few interaction blocks (e.g., a configuration known as MACE-MP-f4) [3].
• Fine-Tuning: Unfreeze and update only the remaining, higher-level layers of the model. This allows the model to adapt its more specialized representations to the new task while retaining the general, low-level features learned during pre-training.
• Training: Proceed with a standard training loop, but note that the number of trainable parameters is substantially reduced, similar to LoRA-based methods.

This approach has been shown to achieve accuracy comparable to models trained from scratch on thousands of data points using only hundreds of target configurations (10-20% of the data), demonstrating exceptional data efficiency [3].

Integrated Software and Workflow Tools

To lower the barriers for researchers, integrated platforms like MatterTune have been developed. MatterTune is a user-friendly framework that provides standardized, modular abstractions for fine-tuning various atomistic foundation models [2].

• Supported Models: It supports a wide range of state-of-the-art models, including ORB, MatterSim, JMP, MACE, and EquiformerV2, within a unified interface [2].
• Workflow Integration: The platform simplifies the entire fine-tuning pipeline, from data handling and model configuration to distributed training and application to downstream tasks like molecular dynamics and property screening [2].
• Customization: Despite its ease of use, MatterTune maintains flexibility, allowing researchers to customize fine-tuning procedures, including the implementation of PEFT methods like those discussed here [2].

The following diagram illustrates the high-level software workflow within such a platform:

The adoption of Parameter-Efficient Fine-Tuning methods, particularly equivariant approaches like ELoRA and MMEA, marks a significant advancement in atomistic materials research. These techniques enable researchers to leverage the power of large foundation models while overcoming critical constraints related to computational cost, data scarcity, and—most importantly—physical consistency. By providing robust performance with a fraction of the parameters, PEFT democratizes access to high-accuracy simulations, paving the way for rapid innovation in drug development, battery design, and novel materials discovery. Integrating these protocols into user-friendly platforms like MatterTune further accelerates this progress, empowering scientists to focus on scientific inquiry rather than computational overhead.

The emergence of atomistic foundation models (AFMs) represents a paradigm shift in computational materials science and chemistry. These models, pre-trained on vast and diverse datasets of quantum mechanical calculations, learn fundamental, transferable representations of atomic interactions [36] [2]. However, a significant challenge persists: achieving quantitative accuracy for specific systems and properties often requires adapting these general-purpose models to specialized downstream tasks [14] [3]. Fine-tuning—the process of further training a pre-trained model on a smaller, application-specific dataset—has emerged as a critical technique to bridge this gap, enabling researchers to leverage the broad knowledge of foundation models while attaining the precision needed for predictive simulations [14] [3].

Despite its promise, the widespread adoption of fine-tuning has been hampered by technical barriers. The ecosystem of atomistic foundation models is fragmented, with each model often having distinct architectures, data formats, and training procedures [36] [14]. This lack of standardization forces researchers to navigate a complex landscape of software tools, creating inefficiency and limiting reproducibility. To address these challenges, integrated software frameworks have been developed. This application note focuses on two such frameworks: MatterTune, an integrated platform for fine-tuning diverse AFMs for broad materials informatics tasks, and the aMACEing Toolkit, a unified interface specifically designed for fine-tuning workflows across multiple machine-learning interatomic potential (MLIP) frameworks [36] [14]. These toolboxes are designed to lower the barriers to adoption, streamline workflows, and facilitate robust, reproducible fine-tuning strategies in materials foundation model research.

MatterTune: A Unified Platform for Atomistic Foundation Models

MatterTune is designed as a modular and extensible framework that simplifies the process of fine-tuning various atomistic foundation models and integrating them into downstream materials informatics and simulation workflows [36] [2]. Its core objective is to provide a standardized, user-friendly interface that abstracts away the implementation complexities of different models, thereby accelerating research and development.

Core Architecture and Abstractions: MatterTune's architecture is built around several key abstractions that ensure flexibility and generalizability [2] [37]:

• Data Abstraction: It employs a minimal data abstraction, defining a dataset as a mapping from sample indices to atomic structures standardized in the ase.Atoms format (from the Atomic Simulation Environment). This provides unified support for numerous input formats during training and inference.
• Property Abstraction: A property schema system separates the specification of physical properties (e.g., energy, forces, custom properties) from their model implementation. This allows users to declaratively specify target properties without dealing with low-level details, while ensuring type safety and physical constraints.
• Backbone Abstraction: This provides unified functional interfaces for different model backbones (e.g., ORB, MatterSim, MACE), regardless of their underlying architecture. Key functions include model_forward for forward propagation and atoms_to_data for converting input structures into the model's required format.

Modular Subsystems: The framework is decoupled into four primary subsystems [2]:

• Model Subsystem: Manages the atomistic FMs, leveraging the backbone and property abstractions to allow users to specify the model and desired output properties easily.
• Data Subsystem: Handles data loading, processing, and conversion between various materials science data formats and a universal internal representation.
• Trainer Subsystem: Integrated with PyTorch Lightning, this subsystem manages the training loop, validation, and checkpointing. It supports various optimizers (Adam, AdamW, SGD), learning rate schedulers (linear, cosine, etc.), and advanced techniques like Exponential Moving Average (EMA).
• Application Subsystem: Provides easy-to-use interfaces for deploying fine-tuned models, including an ASE calculator for molecular dynamics and a MatterTunePropertyPredictor for batch property prediction.

Table 1: Supported Foundation Models in MatterTune

Model	Architecture Type	Notable Features	Primary Training Objective
ORB [36] [2]	Invariant, Non-Conservative	Direct force prediction; denoising pre-training [14]	Denoising + Energy, Forces, Stress
MatterSim [36] [2]	Invariant Graph Neural Network	Universal potential across periodic table [14]	Energy, Forces, Stress
MACE [36] [2]	Equivariant Message Passing	Incorporates higher-body-order interactions [3]	Energy, Forces, Stress
JMP [2]	-	Trained on very large datasets (120M samples) [2]	Energy, Forces
EquiformerV2 [2]	Equivariant Transformer	Scalable attention-based architecture [2]	Energy, Forces, Stress

The aMACEing Toolkit: A Unified Interface for MLIP Fine-Tuning

The aMACEing Toolkit was introduced to address the challenge of fine-tuning machine-learned interatomic potentials (MLIPs) across different architectures [14]. It provides a unified command-line interface that streamlines fine-tuning workflows for multiple leading MLIP frameworks, including MACE, GRACE, SevenNet, MatterSim, and ORB.

The toolkit's primary value lies in its ability to handle framework-specific complexities—such as training data formatting, training setup, model conversion, and performance evaluation—through a consistent interface [14]. This allows researchers to focus on their scientific questions rather than the technical implementation details of each potential. Benchmarking studies using this toolkit have demonstrated that fine-tuning can universally enhance pre-trained models, improving force predictions by factors of 5-15 and energy accuracy by 2-4 orders of magnitude across diverse chemical systems [14].

Quantitative Performance of Fine-Tuned Models

Systematic evaluations demonstrate the profound impact of fine-tuning on the accuracy of foundation models. The following table summarizes key quantitative findings from recent benchmarking studies.

Table 2: Benchmarking Fine-Tuned Foundation Model Performance

Model / Framework	System	Fine-Tuning Method	Key Performance Improvement
MACE-MP-f4 [3]	H₂ on Cu Surfaces	Frozen Transfer Learning (20% data)	Achieved accuracy comparable to from-scratch model trained on 100% of data; superior force accuracy on H atoms [3]
Multiple (MACE, GRACE, etc.) [14]	7 diverse chemical compounds	System-specific fine-tuning	Force errors reduced by 5-15x; energy errors improved by 2-4 orders of magnitude [14]
MACE-MP-f4 [3]	H₂ on Cu Surfaces	Frozen Transfer Learning (Low-data regime)	Outperformed from-scratch models in low-data regime (with as little as 664 configurations) [3]

Experimental Protocols for Fine-Tuning

Protocol 1: Fine-Tuning with MatterTune for Property Prediction

This protocol outlines the steps to fine-tune a foundation model using MatterTune for a downstream property prediction task, such as predicting band gaps or formation energies.

Research Reagent Solutions: Table 3: Essential Materials and Software for MatterTune Fine-Tuning

Item	Function / Description	Example/Reference
Pre-trained Model Weights	Provides the foundational knowledge of atomic interactions.	ORB-v3, MACE-MP-0, MatterSim-v1 [36] [2]
Target Dataset	A curated, system-specific dataset for the fine-tuning task.	MatBench datasets, GNoME data, custom DFT datasets [2]
ASE (Atomic Simulation Environment)	Provides the standardized atoms object for representing structures, crucial for MatterTune's data abstraction [2] [37].
PyTorch Lightning	Simplifies the training loop, distributed training, and checkpointing within the MatterTune trainer subsystem [2].
Validation Dataset	A held-out set used to monitor for overfitting and determine the best model checkpoint during training.

Methodology:

• Data Preparation: Format your target dataset into a collection of atomic structures (ase.Atoms) and the corresponding target property labels. Split the data into training, validation, and test sets.
• Configuration: In MatterTune, specify the fine-tuning job through a configuration file or Python API. Key choices include:
  • Foundation Model: Select from available models (e.g., ORB, MACE).
  • Property Schema: Declare the target properties for the readout head (e.g., "formation_energy").
  • Data Modules: Point to the training and validation datasets.
  • Training Parameters: Define the optimizer (e.g., AdamW), learning rate, scheduler, and batch size.
  • Trainer Settings: Set the number of epochs, validation frequency, and checkpointing rules.
• Execution: Launch the fine-tuning job. MatterTune will handle the data conversion, model setup, and training loop. The training process can be monitored using integrated logging.
• Validation and Application: After training, evaluate the final model on the held-out test set. The fine-tuned model can then be deployed for high-throughput screening or single-point predictions using the MatterTunePropertyPredictor.

Protocol 2: Fine-Tuning for Molecular Dynamics with the aMACEing Toolkit

This protocol describes using the aMACEing Toolkit to fine-tune a foundation MLIP for accurate molecular dynamics simulations, based on benchmarking studies [14].

Methodology:

• Reference Data Generation: Perform short ab initio molecular dynamics (AIMD) trajectories on the system of interest. From these trajectories, sample a set of structures (a few hundred may suffice) and extract the reference energies and forces from the DFT calculations [14].
• Toolkit Setup: Provide the aMACEing Toolkit with the path to the reference data and specify the target MLIP framework (e.g., MACE, MatterSim).
• Unified Fine-Tuning: Execute the toolkit's fine-tuning command. Internally, it will:
  • Convert the reference data into the required format for the chosen MLIP.
  • Set up the training procedure, which typically involves continuing training from the foundation model's weights using the system-specific data.
  • Run the fine-tuning process, often resulting in a significant reduction in force and energy errors [14].
• Model Validation and Deployment: Use the fine-tuned potential to run MD simulations. Critical validation includes comparing dynamic properties (e.g., diffusion coefficients, radial distribution functions) from the MLIP-MD against AIMD benchmarks to ensure the fine-tuned model accurately captures the system's physics [14].

Advanced Technique: Frozen Transfer Learning

For scenarios with very limited data (a few hundred data points), frozen transfer learning is a highly data-efficient fine-tuning strategy, as implemented in tools like the mace-freeze patch for MACE [3].

Methodology:

• Select a Foundation Model: Choose a suitable pre-trained model, such as MACE-MP-0.
• Freeze Model Layers: A significant portion of the model's parameters (especially the earlier layers that capture general chemical features) are frozen. For MACE models, freezing up to the last four interaction layers (MACE-MP-f4) has been shown to be optimal [3].
• Fine-Tune Readout Layers: Only the parameters in the unfrozen layers (typically the later layers and the readout function) are updated during training on the small, target dataset.
• Benefits: This approach prevents catastrophic forgetting of general knowledge, improves training stability, and can achieve accuracy comparable to models trained from scratch on much larger datasets, using only 10-20% of the data [3].

Workflow Visualization

The following diagram illustrates the logical workflow and decision points for fine-tuning atomistic foundation models using the integrated frameworks discussed.

Diagram 1: Fine-tuning workflow for material discovery. This map guides the selection of the appropriate framework (MatterTune or aMACEing) based on the research objective and outlines the subsequent steps in the fine-tuning pipeline.

MatterTune and the aMACEing Toolkit represent a significant advancement in operationalizing atomistic foundation models for specialized research applications. By providing integrated, user-friendly, and reproducible workflows for fine-tuning, these frameworks effectively lower the technical barriers that have hindered widespread adoption. The structured protocols and quantitative evidence presented herein demonstrate that fine-tuning is not merely an incremental improvement but a transformative step that unifies diverse model architectures toward a common goal: achieving near-ab initio accuracy with the computational efficiency of machine learning potentials. As the field progresses, such frameworks will be indispensable for harnessing the full potential of foundation models to accelerate the discovery and design of new materials and molecules.

The accurate prediction of lithium (Li) diffusivity is fundamental to the development of next-generation batteries, influencing key performance metrics such as charging rate, power density, and cycle life. While ab initio methods like Density Functional Theory (DFT) provide chemical accuracy, their computational expense prohibits the simulation of large systems or long timescales relevant to battery operation. Foundational Machine-Learned Interatomic Potentials (MLIPs), pre-trained on diverse materials databases, offer a powerful alternative but often lack the specialized accuracy required for predicting system-specific properties like Li-ion migration barriers and diffusion coefficients in complex electrode materials. This application note demonstrates how fine-tuning these foundation models transforms them into specialized tools for predicting lithium diffusivity with near-ab initio accuracy, using LiF and Li-Al alloys as primary case studies.

Fine-Tuning Rationale and Key Concepts

Foundation models in materials science, such as MACE-MP, are trained on broad datasets (e.g., the Materials Project) to achieve generalizability across the periodic table. However, their performance on specific, high-stakes properties like Li diffusion barriers in novel battery materials can be inconsistent. Fine-tuning addresses this by adapting a pre-trained foundation model to a specific chemical system or phenomenon, using a small, targeted dataset. This process transfers the model's general knowledge of atomic interactions while specializing its predictive capability for the task at hand.

The primary strategies for fine-tuning MLIPs include:

• Full Fine-Tuning: All model weights are updated during training on the new data. This can achieve high accuracy but risks "catastrophic forgetting" of general knowledge and may overfit on small datasets.
• Frozen Transfer Learning: Specific layers of the neural network (typically the early, feature-extraction layers) are "frozen" and not updated during training. Only the later layers are adjusted, preserving the general representations learned during pre-training. This approach has proven highly data-efficient and robust [3].
• Multi-Head Fine-Tuning: An architecture that allows a single model to be trained on data from multiple levels of theory or to maintain performance across a wider range of systems from the foundational training set [3].

For property-critical applications like lithium diffusivity, frozen transfer learning has emerged as a particularly effective strategy, enabling high accuracy with minimal data by building upon the foundational model's established knowledge base [3].

Case Study 1: Fine-Tuning for Li Diffusion in LiF

Background and Objective

Lithium Fluoride (LiF) is a key component of the solid electrolyte interphase (SEI) in Li-ion batteries. Understanding Li diffusion within LiF, especially interstitial diffusion, is critical for optimizing battery kinetics and longevity. The objective was to fine-tune a foundational MACE model to accurately predict the activation energy (Ea) of interstitial Li diffusion in LiF and compare its performance to a high-quality DeePMD potential trained from scratch on a large dataset [38].

Quantitative Results

Fine-tuning the MACE-MPA-0 model dramatically improved its predictive accuracy for Li diffusivity with minimal data.

Table 1: Fine-Tuning Performance for Li Diffusion in LiF [38]

Model	Training Data Size	Predicted Activation Energy (Ea)	Reference Ea (DeePMD)
MACE-MPA-0 (Foundational)	0 data points (Zero-shot)	0.22 eV	0.24 eV
MACE (Fine-tuned)	300 data points	0.20 eV	0.24 eV
DeePMD (From Scratch)	> 40,000 data points	0.24 eV	0.24 eV

Experimental Protocol

Protocol 3.3.1: Fine-Tuning an MLIP for Li Diffusivity in LiF

Objective: Adapt a foundational MACE model to achieve quantitative accuracy in predicting interstitial Li diffusion properties in LiF.

Materials and Computational Resources:

• Foundation Model: MACE-MPA-0 or similar MACE-MP model [38] [3].
• Target System: Crystalline LiF with interstitial Li defects.
• Reference Data Source: Ab initio molecular dynamics (AIMD) or DFT nudged elastic band (NEB) calculations.
• Software: MACE codebase with fine-tuning capabilities (e.g., incorporating mace-freeze patch for frozen transfer learning) [3].
• Computing Hardware: High-performance computing cluster with GPUs.

Procedure:

• Reference Data Generation:
  • Perform AIMD simulations of LiF with interstitial Li defects at relevant temperatures.
  • Alternatively, use DFT-NEB calculations to map the diffusion pathway and energy barrier.
  • Extract a dataset of atomic configurations, including their reference energies and forces as calculated by DFT. A few hundred diverse configurations are often sufficient [38].

• Data Preparation:

  • Format the dataset (structures, energies, forces) according to the requirements of the MACE training pipeline.

• Fine-Tuning Setup:

  • Initialize the training process with the weights from the pre-trained MACE-MPA-0 model.
  • Strategy: Employ a frozen transfer learning approach. Freeze the parameters in the initial interaction layers and the embedding layers of the network (e.g., the first 4 interaction blocks in a MACE-MP "small" model), allowing only the later layers (readouts and final interaction layers) to be updated [3].
  • Configure training hyperparameters: use a low initial learning rate (e.g., 1e-4), a small batch size, and a conservative number of training epochs to prevent overfitting.

• Model Training:

  • Execute the training loop, periodically validating the model on a held-out subset of the generated data to monitor for overfitting.

• Validation and Testing:

  • Use the fine-tuned model to run molecular dynamics simulations of Li diffusion in LiF.
  • Calculate the mean-squared displacement (MSD) of Li ions and derive the diffusion coefficient and activation energy.
  • Validate the predicted activation energy and other dynamic properties against the original DeePMD and DFT reference values.

Case Study 2: Fine-Tuning for Li-Al Alloy Electrodes

Background and Objective

Li-Al alloys are promising negative electrode materials for all-solid-state batteries. Their performance is governed by a stark difference in Li diffusivity between the Li-poor α-phase (LixAl1, x ≤ 0.05) and the Li-rich β-phase (LixAl1, 0.95 ≤ x ≤ 1). First-principles calculations estimate the Li diffusion coefficient in the β-phase is ten orders of magnitude higher (~10⁻⁷ cm²/s) than in the α-phase (~10⁻¹⁷ cm²/s) [39]. Accurately modeling this discrepancy and the diffusion across phase boundaries is essential for electrode design but challenging for general-purpose foundation models. Fine-tuning was used to create a specialized potential for this system.

Key Scientific Insight

The ultra-fast Li diffusion in the β-LiAl phase arises from two factors: low migration barriers for Li hops (around 100 meV) and an unusually high concentration of vacancies in the crystal structure. In contrast, Li diffusion in the α-phase is sluggish due to high migration barriers and a low equilibrium vacancy concentration [39].

Experimental Protocol

Protocol 4.3.1: Fine-Tuning for Phase-Dependent Diffusion in Alloys

Objective: Specialize a foundational MLIP to capture the vast difference in Li diffusivity between the α and β phases of LixAl1 and model diffusion across their interfaces.

Materials and Computational Resources:

• Foundation Model: A suitable foundational MLIP (e.g., from the MACE or CHGNet families) [3] [8].
• Target Systems: Atomic configurations of α-Al, β-LiAl, and α/β phase boundaries.
• Reference Data: DFT calculations of formation energies, vacancy energies, and NEB-calculated migration barriers for Li in both phases.

Procedure:

• Targeted Data Generation:
  • Use DFT to calculate the energy and forces for a set of configurations that comprehensively sample the local environments in both α and β phases.
  • Crucially, include configurations with Li vacancies in the β-phase and transition states along Li migration paths in both phases, as identified by NEB calculations.
  • Generate configurations that model the interface between the α and β phases.

• Fine-Tuning with Partial Freezing:

  • Load the pre-trained foundation model.
  • Apply a frozen transfer learning strategy, freezing the lower-level network layers responsible for learning general elemental interactions.
  • Fine-tune the model on the generated LixAl1 dataset, allowing the higher-level layers to specialize in the unique chemistry and defect properties of the Li-Al system.

• Model Evaluation:

  • Validate the fine-tuned model by comparing its predictions of Li vacancy formation energies and migration barriers in both phases against DFT results.
  • Run MD simulations to compute the Li diffusion coefficients in the α and β phases separately and confirm they match the expected orders of magnitude.
  • Simulate Li transport across a model α/β interface to ensure the potential correctly captures the interfacial kinetics.

Universal Workflow for Fine-Tuning MLIPs

The following diagram illustrates a generalized, hierarchical fine-tuning workflow for foundational MLIPs, adaptable to various battery material systems.

Diagram 1: A universal workflow for fine-tuning MLIPs for battery materials.

Table 2: Essential Computational Tools for Fine-Tuning MLIPs [38] [3] [40]

Tool / Resource	Type	Function in Fine-Tuning Workflow
MACE-MPA-0	Foundational MLIP	A highly performant, equivariant foundation model serving as a starting point for fine-tuning on systems like LiF [38].
MatGL	Software Library	An open-source framework providing pre-trained models (e.g., M3GNet) and tools for training and fine-tuning graph neural networks for materials [40].
mace-freeze patch	Software Tool	A patch to the MACE code that enables frozen transfer learning by allowing specific layers of the model to be fixed during training [3].
aMACEing Toolkit	Software Toolkit	A unified interface designed to simplify and standardize fine-tuning workflows across different MLIP frameworks (MACE, GRACE, etc.) [14].
Materials Project (MPtrj)	Training Dataset	A large, publicly available database of DFT calculations on inorganic materials used to pre-train many foundational MLIPs [3] [14].
CP2K	Simulation Software	A versatile quantum chemistry and solid-state physics software package used for generating reference DFT data for fine-tuning [41].

Fine-tuning has emerged as a critical methodology for unlocking the full potential of foundational MLIPs in specialized domains like battery materials research. As demonstrated in the cases of LiF and Li-Al alloys, strategies like frozen transfer learning enable researchers to achieve chemical accuracy for complex properties such as lithium diffusivity, while requiring only a fraction of the data needed to train a model from scratch. By leveraging established workflows and tools, scientists can rapidly develop specialized, high-fidelity simulation capabilities to accelerate the design and optimization of next-generation energy storage materials.

The ability to accurately simulate polymorphic phase transitions in organic molecular crystals is a critical challenge in materials science and pharmaceutical development. These transitions, where a crystal can reversibly change between different solid forms (polymorphs), directly impact material properties, drug stability, and bioavailability. Predicting and capturing these phenomena with classical force fields or ab initio methods alone has been limited by a fundamental trade-off between computational efficiency and chemical accuracy [14].

The emergence of atomistic foundation models (FMs)—machine-learned interatomic potentials (MLIPs) pre-trained on vast quantum chemical datasets—presents a transformative opportunity. These models, including MACE-MP, CHGNet, MatterSim, and ORB, learn general, transferable representations of atomic interactions from large-scale data repositories like the Materials Project [3] [2]. However, while robust for many systems, these general-purpose potentials can fail to capture the subtle, system-specific energy landscapes and collective dynamics of polymorphic transitions in organic crystals [42] [43].

This case study demonstrates that targeted fine-tuning of foundation models enables the accurate and efficient simulation of reversible polymorphic phase transitions, a task that often eludes their out-of-the-box capabilities. We detail a protocol for applying Frozen Transfer Learning to the MACE-MP foundation model, systematically evaluating its performance on the α⇌β transition in the prototypical organic crystal 2,4,5-triiodo-1H-imidazole (tIIm) [42].

Foundation Models and Fine-Tuning Strategies

Atomistic foundation models are typically Graph Neural Networks (GNNs) that map atomic structures to properties like energy and forces. Pre-trained on diverse datasets encompassing millions of Density Functional Theory (DFT) calculations, they learn fundamental, transferable representations of atomic interactions [2]. The table below summarizes key models relevant to molecular crystals.

Table 1: Key Atomistic Foundation Models for Materials Research

Model Name	Key Architectural Features	Pre-training Dataset(s)	Notable Capabilities
MACE-MP [3]	Many-body equivariant messages	Materials Project (MPtrj)	High accuracy on inorganic and molecular systems
CHGNet [3]	Graph neural network with charge features	Materials Project	Incorporates magnetic moments
MatterSim [2]	Invariant graph network (M3GNet-based)	Proprietary dataset (0-5000 K, 0-1000 GPa)	Universal potential for broad conditions
ORB [2] [14]	Non-conservative, invariant network	Open Materials, Open Molecules	Direct force prediction (no energy gradient)
GNoME [2]	Equivariant transformer	16.2M structures	Extensive materials space exploration

Fine-Tuning Strategies for Specialized Applications

While foundational, these models can be further specialized. Fine-tuning (or transfer learning) is the process of adapting a pre-trained FM to a specific system or phenomenon using a smaller, targeted dataset [2]. This is especially crucial for capturing rare events like phase transitions, which are often underrepresented in broad training sets [42].

Table 2: Comparison of Fine-Tuning Methods for Atomistic Foundation Models

Fine-Tuning Method	Core Principle	Key Advantages	Reported Data Efficiency
Frozen Transfer Learning (MACE-freeze) [3]	Freezes initial layers of the network; only updates later layers (e.g., readouts).	Prevents "catastrophic forgetting," retains general features, reduces training cost.	Achieves target accuracy with 10-20% of the data required for training from scratch.
Parameter-Efficient Equivariant Low-Rank Adaptation (ELoRA) [42]	Adds and trains small, low-rank adapters to the model structure.	Highly parameter-efficient, preserves original model weights, robust for complex transitions.	Enables simulation of full transition with a limited target dataset [42].
Naive Fine-Tuning	Continues training all parameters of the pre-trained model on new data.	Simple to implement.	High risk of overfitting and catastrophic forgetting [3].
Multi-Head Fine-Tuning [3]	Attaches multiple output heads for different levels of theory or systems.	Maintains performance across original training domain.	Higher complexity; data efficiency depends on implementation.

For the challenging task of modeling the reversible α⇌β transition in tIIm, a recent study found that while off-the-shelf FMs (MACE-MP-0, MACE-OFF-small, SevenNet, CHGNet) failed, fine-tuning—particularly with the ELoRA method—successfully recovered the full collective dynamics and revealed a stepwise transition pathway with asymmetric energy barriers [42].

Application Note: Fine-Tuning for the tIIm α⇌β Transition

Experimental Setup and Workflow

The following diagram outlines the integrated workflow for fine-tuning a foundation model and applying it to simulate a polymorphic phase transition.

Quantitative Performance of Fine-Tuned Models

Systematic benchmarking reveals that fine-tuning dramatically enhances model performance. A large-scale study of five MLIP frameworks (MACE, GRACE, SevenNet, MatterSim, ORB) showed consistent improvements across chemically diverse systems after fine-tuning [14].

Table 3: Benchmarking Fine-Tuning Performance Across MLIP Architectures [14]

Model Architecture	Foundation Model Force RMSE (meV/Å)	Fine-Tuned Model Force RMSE (meV/Å)	Improvement Factor
Equivariant (MACE)	251 - 438	21 - 58	5x - 15x
Equivariant (GRACE)	261 - 421	28 - 55	5x - 15x
Equivariant (SevenNet)	249 - 411	31 - 61	5x - 13x
Invariant (MatterSim)	271 - 452	35 - 65	5x - 13x
Non-Conservative (ORB)	241 - 445	29 - 63	5x - 15x

The data demonstrates that fine-tuning is a universal strategy, achieving order-of-magnitude improvements in force prediction accuracy regardless of the underlying MLIP architecture (equivariant/invariant, conservative/non-conservative) [14]. For the tIIm system, fine-tuning was the decisive factor enabling the accurate simulation of the complete, reversible transition pathway, which was not possible with any of the four tested foundation models out-of-the-box [42].

Detailed Experimental Protocols

Protocol 1: Fine-Tuning a Foundation Model with Frozen Transfer Learning

This protocol adapts the "MACE-freeze" method for fine-tuning the MACE-MP model [3].

Research Reagent Solutions

• Foundation Model: MACE-MP-0 "small" model. Serves as the robust, pre-trained base.
• Fine-Tuning Dataset: 300-500 DFT configurations of tIIm. Can be generated via short AIMD runs at temperatures near the phase transition or by sampling from both polymorphs.
• Software: MACE codebase with the mace-freeze patch [3]. Python, ASE.
• Computing Resources: GPU node (e.g., NVIDIA A100 or V100) with ≥ 32 GB VRAM.

Step-by-Step Procedure

• Data Preparation
  • Generate or obtain your target dataset of atomic configurations (.extxyz format is standard).
  • Split the data into training (80%), validation (10%), and test (10%) sets.
  • Ensure the data includes target energies and forces for each configuration.

• Model and Patch Setup

  • Install the MACE software suite.
  • Apply the mace-freeze patch, which enables layer freezing functionality [3].

• Fine-Tuning Configuration

  • Initialize the model using the pre-trained weights of MACE-MP-0 "small".
  • In the configuration, set freeze_layers = ["interaction_0", "interaction_1", ...] to freeze the first several interaction layers. The MACE-MP-f4 model (freezing the first four interaction layers) has been shown to be optimal for data efficiency and accuracy [3].
  • Configure the readout layers to remain trainable.
  • Set training hyperparameters: a low initial learning rate (e.g., 1e-4), use the Adam optimizer, and employ a learning rate scheduler that reduces the rate on validation loss plateau.

• Training and Validation

  • Run the training procedure, monitoring the loss on both training and validation sets.
  • The training should be stopped when the validation loss plateaus or begins to increase, indicating potential overfitting.
  • The final model should be saved from the epoch with the lowest validation loss.

Protocol 2: Simulating the Phase Transition Pathway

This protocol uses the fine-tuned model to capture the polymorphic transition.

Research Reagent Solutions

• Fine-Tuned Model: The model output from Protocol 1.
• Simulation Software: LAMMPS or ASE with MACE interface.
• Analysis Tools: Code for calculating Collective Variables (CVs) like SOAP descriptors or symmetry-adapted order parameters.

Step-by-Step Procedure

• System Equilibration
  • Create initial simulation cells for the α and β polymorphs of tIIm.
  • Using the fine-tuned model, run NPT molecular dynamics (MD) to equilibrate each phase at the target temperature and pressure.

• Enhanced Sampling Setup

  • To overcome the high free energy barrier of the phase transition, employ an enhanced sampling method. Metadynamics or Umbrella Sampling are suitable choices.
  • Define one or two Collective Variables (CVs) that distinctively describe the two polymorphs. For organic crystals, this could be a combination of:
    • A symmetry-adapted order parameter that distinguishes the space groups.
    • A CV describing molecular orientation within the unit cell.
    • The unit cell angles or ratios.

• Sampling Simulation

  • Run the enhanced sampling simulation, starting from one polymorph (e.g., α-tIIm).
  • In metadynamics, the history-dependent bias will push the system over the energy barrier and facilitate the transition to the other polymorph (β-tIIm).
  • For a reversible transition, continue the simulation until several transitions back and forth are observed.

• Pathway and Mechanism Analysis

  • From the simulation trajectory, analyze the evolution of the CVs and the atomic structure to identify the transition mechanism.
  • The free energy surface as a function of the CVs can be reconstructed from the simulation data (e.g., from metadynamics).
  • Identify any metastable intermediate states along the pathway. For tIIm, fine-tuned models revealed a stepwise pathway with a pronounced asymmetry in the energy barriers between the α→β and β→α directions [42].

The Scientist's Toolkit

This section details the essential resources for implementing the described workflows.

Table 4: Essential Research Reagents and Software Tools

Item Name	Specifications / Version	Function / Application	Source / Availability
MACE-MP-0	"small", "medium", or "large" variants	A high-performance, equivariant foundation model for atomistic simulations. Serves as the starting point for fine-tuning.	https://github.com/ACEsuit/mace
MatterTune	v1.0+	An integrated, user-friendly platform for fine-tuning various atomistic FMs (ORB, MatterSim, MACE, etc.), lowering adoption barriers [2].	https://github.com/Fung-Lab/MatterTune
aMACEing Toolkit	As per release	A unified interface for fine-tuning workflows across multiple MLIP frameworks, promoting reproducibility and ease of use [14].	Information included with reference [14]
SPaDe-CSP Workflow	N/A	A machine learning-based workflow for Crystal Structure Prediction that uses NNPs for efficient structure relaxation, complementary to phase transition studies [44].	Methodology described in reference [44]
Fine-Tuning Dataset (tIIm)	~500 configurations	A targeted dataset for adapting a foundation model to the specific energy landscape of 2,4,5-triiodo-1H-imidazole.	Generated via AIMD as per protocol [42]
ASE (Atomic Simulation Environment)	v3.22.1+	A Python package for setting up, managing, visualizing, and analyzing atomistic simulations. Works with many MLIPs.	https://wiki.fysik.dtu.dk/ase/
LAMMPS	Stable release 2Aug2023+	A classical molecular dynamics simulator with growing support for MLIPs, used for running large-scale MD with fine-tuned models.	https://www.lammps.org/

This case study establishes that fine-tuning is not merely an optional optimization but a critical step for enabling atomistic foundation models to simulate complex, collective phenomena like polymorphic phase transitions in organic crystals. The outlined protocols for Frozen Transfer Learning provide a concrete, data-efficient pathway to achieve near-ab initio accuracy where off-the-shelf foundation models fall short.

The resulting fine-tuned models successfully capture the reversible α⇌β transition in tIIm, revealing detailed mechanistic insights into the stepwise pathway and asymmetric energy barriers [42]. This capability has profound implications for pharmaceutical development, where predicting and controlling polymorphism is essential for ensuring drug stability and efficacy. As foundation models and fine-tuning tools like MatterTune [2] and the aMACEing Toolkit [14] continue to mature and become more accessible, they promise to significantly accelerate the discovery and design of novel functional molecular materials.

Overcoming Common Pitfalls and Optimizing Performance

Combating Catastrophic Forgetting with Multi-Head and Frozen Fine-Tuning

In materials science, foundation models pre-trained on extensive datasets, such as those in the Materials Project (MPtrj), provide a powerful starting point for atomistic simulations [3]. However, a significant challenge emerges when these models are fine-tuned for specialized tasks: catastrophic forgetting (CF). This phenomenon describes a model's tendency to lose previously acquired knowledge when learning new information, which is particularly detrimental when foundational chemical and structural understanding is overwritten during specialization on a narrow dataset [45] [46].

This Application Note details two advanced fine-tuning strategies—Multi-Head Fine-Tuning and Frozen Fine-Tuning—explicitly designed to mitigate catastrophic forgetting within materials foundation models. We provide quantitative performance comparisons and step-by-step experimental protocols to guide researchers in implementing these methods, ensuring robust and data-efficient model adaptation for specialized applications such as surface chemistry and alloy design.

Quantitative Comparison of Fine-Tuning Strategies

The table below summarizes the key characteristics and performance metrics of the two primary fine-tuning strategies discussed in this note, based on benchmark studies.

Table 1: Comparison of Fine-Tuning Strategies for Mitigating Catastrophic Forgetting

Fine-Tuning Strategy	Core Principle	Reported Data Efficiency	Key Performance Metrics	Best-Suited Applications
Multi-Head Fine-Tuning [3]	Adds task-specific output "heads" to a frozen or partially frozen model backbone.	Enables training on data from multiple levels of electronic structure theory.	Maintains transferability across diverse systems in the pre-training dataset (e.g., MPtrj).	Multi-task learning environments; preserving broad transferability.
Frozen Fine-Tuning (MACE-freeze) [3]	Freezes a portion of the model's layers (e.g., lower-level weights and biases) during fine-tuning.	Achieves high accuracy with only 10–20% of the original training data (hundreds of data points).	Force RMSE similar to from-scratch models trained on 100% of data (thousands of points). [3]	Data-scarce scenarios; rapid adaptation for specific systems (e.g., H₂/Cu surfaces, ternary alloys).

Detailed Experimental Protocols

Protocol A: Frozen Fine-Tuning with MACE-freeze

This protocol outlines the procedure for fine-tuning a MACE-MP foundation model using the frozen transfer learning method, which has demonstrated high data efficiency [3].

1. Prerequisite Model and Software Setup

• Foundation Model: Obtain a pre-trained MACE-MP model ("small," "medium," or "large") [3] [47].
• Software: Install the MACE software suite and the mace-freeze patch, which enables layer freezing [3].
• Environment: Ensure access to a Python environment with libraries such as ASE for atomistic simulations [47].

2. Dataset Preparation and Curation

• Target System Data: Prepare a dataset of atomic structures (e.g., from DFT calculations) relevant to your specific task. For example, a dataset for H₂ dissociation on Cu surfaces contains 4,230 structures [3].
• Data Splitting: Partition the dataset into training (e.g., 80%) and validation (e.g., 20%) sets.

3. Model Configuration and Freezing

• Layer Selection: Choose which layers of the foundation model to freeze. Benchmark studies indicate that freezing the first four layers (MACE-MP-f4 configuration) offers an optimal balance between accuracy and computational cost [3].
• Implementation: Use the mace-freeze patch to apply the freezing configuration, preventing updates to the weights and biases in the selected layers during training.

4. Hyperparameter Selection and Training Loop

• Learning Rate: Use a reduced learning rate compared to from-scratch training to facilitate stable convergence.
• Loss Function: Employ a loss function that combines energy and force predictions.
• Execution: Run the training loop, monitoring loss on both training and validation sets.

5. Validation and Analysis

• Quantitative Metrics: Calculate the Root Mean Square Error (RMSE) on energies and forces for the validation set.
• Benchmarking: Compare the performance of the fine-tuned model against a from-scratch MACE model trained on the same dataset and the original foundation model.

Protocol B: Implementing Multi-Head Fine-Tuning

This protocol describes the process for employing a multi-head architecture to maintain performance on previous tasks while learning new ones [3].

1. Architecture Modification

• Backbone Model: Start with a pre-trained foundation model (e.g., MACE-MP).
• Add Task-Specific Heads: Attach multiple independent output layers ("heads") to the shared backbone. Each head is responsible for predictions for a specific task or dataset.

2. Training Procedure for New Tasks

• Freeze Backbone: Keep the parameters of the shared backbone model frozen to protect the foundational knowledge.
• Activate Corresponding Head: When training on a new task, only update the parameters of the task-specific head associated with that task.

3. Inference and Deployment

• Head Selection: At inference time, select the appropriate pre-trained head for the desired task.
• Forward Pass: Pass input data through the shared backbone and the selected head to obtain predictions.

Workflow Visualization

The following diagram illustrates the logical structure and data flow for the two fine-tuning strategies, highlighting how they protect foundational knowledge.

The Scientist's Toolkit: Research Reagent Solutions

Table 2: Essential Software and Model Components for Fine-Tuning

Item Name	Type	Function in Experiment	Example / Source
MACE-MP Foundation Model	Pre-trained Model	Provides a universal, pre-trained base for interatomic potentials.	MACE-MP-0 model [47]
mace-freeze Patch	Software Tool	Enables layer freezing during fine-tuning of MACE models.	MACE software suite patch [3]
ASE (Atomic Simulation Environment)	Python Library	Facilitates setting up, running, and analyzing atomistic simulations.	https://wiki.fysik.dtu.dk/ase/ [47]
RBMD Package	Simulation Platform	Enables large-scale particle simulations integrated with MLIPs.	Random Batch Molecular Dynamics [47]
PEFT Libraries	Code Library	Provides implementations of Parameter-Efficient Fine-Tuning methods like LoRA.	Hugging Face PEFT Library [45]

The application of machine learning (ML) in atomistic materials simulation has long been constrained by a significant data bottleneck. Traditional machine-learned interatomic potentials (MLIPs) often require thousands of expensive first-principles calculations to achieve the high accuracy necessary for predicting critical properties like reaction barriers, phase transitions, and material stability [3]. This substantial data requirement places atomistic modeling beyond reach for many research groups studying complex or novel systems where generating extensive training data is computationally prohibitive.

The emergence of foundation models represents a paradigm shift in this landscape. These models are large-scale machine learning systems pre-trained on vast and diverse datasets, embodying general knowledge of atomic interactions across broad chemical spaces [48] [49]. In materials science, foundation models such as MACE-MP-0, CHGNet, and MatterSim have been trained on millions of density functional theory (DFT) calculations from repositories like the Materials Project, Open Materials, and Alexandria databases [14] [50]. While these models demonstrate impressive transferability, their out-of-the-box accuracy often remains insufficient for predicting subtle energetic differences in specialized applications [3] [42].

Fine-tuning has emerged as a powerful technique to bridge this accuracy gap while maintaining data efficiency. By adapting a pre-trained foundation model to a specific system or property with a small, targeted dataset, researchers can achieve high accuracy with orders of magnitude less data than training from scratch [14]. This approach leverages the general physical representations learned during pre-training while specializing the model for a particular task. The resulting fine-tuned models can achieve chemical accuracy with only hundreds of data points – a significant improvement over conventional MLIPs that typically require thousands of training structures [3] [50].

Quantifying Data Efficiency: Performance with Limited Data

Recent benchmarking studies across diverse chemical systems have consistently demonstrated that fine-tuned foundation models achieve high accuracy with dramatically reduced data requirements compared to training models from scratch.

Table 1: Data Efficiency of Fine-Tuned Foundation Models Across Various Applications

System/Property	Foundation Model	Fine-tuning Data Size	Key Results	Reference
H₂/Cu Surface Reactions	MACE-MP	664 configurations (20% of full set)	Similar accuracy to from-scratch model trained on 3,376 configurations	[3]
Ice Polymorph Sublimation Enthalpies	MACE-MP-0	~50 training structures	Sub-kJ/mol accuracy in sublimation enthalpies; <1% error in densities	[51] [50]
Diverse Chemical Systems	MACE, GRACE, SevenNet, MatterSim, ORB	Hundreds of structures from short AIMD	Force errors reduced 5-15x; energy errors improved 2-4 orders of magnitude	[14]
Organic Molecular Crystal Phase Transition	MACE-MP-0, MACE-OFF, SevenNet, CHGNet	Limited data from targeted sampling	Robust simulation of reversible α⇌β polymorphic phase transition	[42]

The data in Table 1 illustrates a consistent pattern: fine-tuned foundation models consistently achieve high accuracy with datasets comprising only hundreds of data points across diverse applications. For the challenging task of predicting sublimation enthalpies of molecular crystal polymorphs – which requires sub-kJ/mol accuracy – fine-tuning the MACE-MP-0 model with approximately 50 training structures achieved first-principles quality predictions [50]. Similarly, for modeling reactive chemistry at surfaces, fine-tuned models using only 20% of the full dataset (hundreds of data points) achieved similar accuracy to models trained from scratch on the complete dataset (thousands of data points) [3].

A particularly comprehensive study benchmarking five leading MLIP frameworks (MACE, GRACE, SevenNet, MatterSim, and ORB) across seven chemically diverse compounds revealed that fine-tuning universally enhanced performance, reducing force errors by factors of 5-15 and improving energy accuracy by 2-4 orders of magnitude [14]. This convergence in performance across architectures after fine-tuning suggests that the approach is universally applicable, regardless of the specific foundation model architecture.

Core Methodologies for Data-Efficient Fine-Tuning

Frozen Transfer Learning

Frozen transfer learning with partially frozen weights and biases has emerged as a particularly effective strategy for data-efficient fine-tuning of foundation models for interatomic potentials [3]. This approach involves keeping the parameters of specific model layers fixed during fine-tuning, allowing only a subset of parameters to adapt to the new data.

Table 2: Frozen Transfer Learning Configurations for MACE Models

Model Variant	Frozen Layers	Trainable Parameters	Performance Characteristics	Recommended Use Cases
MACE-MP-f6	All except readouts	Minimal	Good in very low-data regime but limited flexibility	Extremely data-scarce scenarios (<100 data points)
MACE-MP-f5	Product layer and readouts	Moderate	Improved performance over f6	Limited data availability (100-300 data points)
MACE-MP-f4	Interaction layers, product layer, and readouts	Substantial	Peak performance in low-data regime; optimal balance	General purpose; 300-1,000 data points
MACE-MP-f0	All layers active	All parameters	Similar validation errors to f4 but higher computational cost	When data is less constrained (>1,000 data points)

The "frozen" approach maintains the general physical representations learned during pre-training while adapting the higher-level task-specific layers. Studies have demonstrated that models with four frozen layers (MACE-MP-f4) achieve optimal performance in low-data regimes, outperforming both more heavily frozen models and fully trainable models when fine-tuning data is limited [3]. This configuration retains the transferable features learned from large-scale datasets like Materials Project while allowing sufficient flexibility to adapt to system-specific characteristics.

Data Generation and Sampling Protocols

The quality and representativeness of the fine-tuning dataset are crucial factors in achieving high accuracy with limited data. Efficient protocols for generating targeted training data have been developed to maximize information content while minimizing computational cost.

For molecular crystals, an effective approach involves performing short ab initio molecular dynamics (AIMD) simulations at the target temperature and pressure, then equidistantly sampling frames from these trajectories [50]. This strategy ensures adequate sampling of relevant thermodynamic configurations while avoiding redundant similar structures. A typical protocol might involve:

• Initial Structure Preparation: Starting with the experimental or DFT-optimized crystal structure of the target system.
• Short AIMD Simulation: Running a relatively short (tens of picoseconds) AIMD simulation under the thermodynamic conditions of interest (NPT or NVT ensemble).
• Equidistant Frame Sampling: Extracting structures at regular intervals from the trajectory to create a diverse but non-redundant training set.
• Electronic Structure Calculation: Computing accurate energies and forces for each sampled structure using the target level of theory (DFT, RPA, etc.).

This approach typically generates sufficient training data (tens to hundreds of structures) to fine-tune foundation models for accurate property prediction [50]. For reactive systems like gas-surface dynamics, uncertainty-driven active learning algorithms can identify the most informative configurations to include in the training set, further enhancing data efficiency [3].

Experimental Protocols: Step-by-Step Implementation

Protocol 1: Fine-tuning for Molecular Crystal Properties

This protocol details the procedure for fine-tuning foundation models to predict sublimation enthalpies and physical properties of molecular crystals, adapted from studies on ice polymorphs [50].

Research Reagent Solutions:

• Foundation Model: Pre-trained MACE-MP-0 model (provides general atomic representations)
• Electronic Structure Code: DFT package (VASP, Quantum ESPRESSO) for reference calculations
• Molecular Dynamics Engine: LAMMPS, i-PI for AIMD simulations
• Fine-tuning Framework: MACE software suite with mace-freeze patch or MatterTune platform

Step 1: Dataset Generation (Target: 50-100 structures)

• Begin with the experimental crystal structure of the target molecular crystal.
• Perform a short NPT AIMD simulation (10-20 ps) at the temperature and pressure of interest using a reliable DFT functional.
• Sample frames equidistantly from the trajectory (every 100-200 fs) to capture diverse atomic environments.
• For each sampled structure, compute the total energy, atomic forces, and stress tensor using the target level of theory.

Step 2: Model Preparation

• Select an appropriate foundation model (MACE-MP-0 recommended for molecular crystals).
• Choose the frozen layer configuration based on available data (MACE-MP-f4 optimal for hundreds of data points).
• Prepare the fine-tuning dataset in the required format (ASE atoms objects or framework-specific format).

Step 3: Fine-tuning Procedure

• Initialize the model with pre-trained foundation model weights.
• Freeze parameters in the lower layers according to the selected configuration.
• Train only the unfrozen layers using the small target dataset.
• Use a conservative learning rate (10⁻⁴ to 10⁻⁵) to avoid catastrophic forgetting.
• Employ early stopping based on validation loss to prevent overfitting.

Step 4: Validation and Deployment

• Validate the fine-tuned model on held-out structures from the AIMD trajectory.
• Verify accuracy on target properties (sublimation enthalpies, densities) against reference calculations.
• Deploy the model for molecular dynamics simulations or property prediction.

Protocol 2: Fine-tuning for Reactive Surface Chemistry

This protocol adapts foundation models for challenging reactive chemistry applications like dissociative adsorption on metal surfaces [3].

Research Reagent Solutions:

• Foundation Model: MACE-MP "small" or "medium" model (balances accuracy and efficiency)
• Active Learning Framework: Uncertainty quantification tools for targeted data acquisition
• Reference Data: High-accuracy DFT calculations of reaction pathways

Step 1: Targeted Data Generation

• Identify key reaction pathways and transition states for the target surface reaction.
• Use committee models or uncertainty quantification to identify underrepresented configurations.
• Generate structures spanning the relevant configuration space (reactants, products, transition states).
• Compute reference energies and forces for these critical configurations.

Step 2: Strategic Fine-tuning

• Employ the MACE-freeze approach with f4 configuration (freezing lower layers).
• If sufficient data is available (hundreds of configurations), use MACE-MP-f0 (all layers tunable).
• Focus validation on reaction barriers and adsorption energies rather than bulk properties.

Step 3: Surrogate Model Creation (Optional)

• Use the fine-tuned foundation model to generate labels for a larger dataset.
• Train a more computationally efficient surrogate model (e.g., Atomic Cluster Expansion) on this dataset.
• This two-step process maintains accuracy while improving computational efficiency for large-scale simulations.

Unified Frameworks for Fine-tuning

The growing complexity of fine-tuning different foundation models has spurred the development of unified frameworks that streamline the process across multiple architectures. MatterTune provides an integrated, user-friendly platform that supports fine-tuning for various state-of-the-art foundation models including ORB, MatterSim, JMP, MACE, and EquiformerV2 [2]. This framework addresses key challenges in the fine-tuning ecosystem:

• Standardization: Provides consistent interfaces and workflows across different model architectures.
• Flexibility: Supports diverse fine-tuning strategies from full model tuning to parameter-efficient approaches.
• Accessibility: Lowers the barrier for researchers to leverage state-of-the-art foundation models without deep expertise in each implementation.

The aMACEing Toolkit represents another approach, offering a unified command-line interface for fine-tuning workflows across multiple MLIP frameworks [14]. These tools significantly reduce the technical overhead of implementing fine-tuning strategies, making data-efficient approaches more accessible to the broader materials science community.

Data-efficient fine-tuning of foundation models represents a transformative approach in computational materials science, dramatically reducing the data requirements for accurate atomistic simulations while maintaining the transferability and physical robustness of pre-trained models. The methodologies outlined in this application note – particularly frozen transfer learning and targeted data sampling – enable researchers to achieve high accuracy with hundreds rather than thousands of data points across diverse applications from molecular crystals to reactive surface chemistry.

As the field evolves, several emerging trends promise to further enhance data efficiency. Parameter-efficient fine-tuning methods like Equivariant Low-Rank Adaptation (ELoRA) are showing promise for adapting foundation models with even fewer tunable parameters [42]. Multi-task fine-tuning approaches that leverage related datasets across different properties may further reduce data requirements. Additionally, the development of more sophisticated uncertainty quantification techniques will enable more intelligent targeted data acquisition, maximizing the information content of each training sample.

The democratization of these techniques through unified frameworks like MatterTune and the aMACEing Toolkit will accelerate their adoption across the materials science community [14] [2]. By making accurate atomistic modeling accessible even for data-scarce systems, these data efficiency strategies have the potential to dramatically accelerate materials discovery and design across application domains from energy storage to pharmaceutical development.

Fine-tuning has emerged as a critical technique for adapting broadly pre-trained materials foundation models to specialized downstream tasks, offering a powerful compromise between the robust transferability of general models and the high accuracy required for system-specific predictions. The core challenge lies in strategically selecting which layers of a neural network to fine-tune. An overly rigid approach, freezing too many layers, can limit the model's ability to adapt to new chemical environments. Conversely, an overly flexible strategy, updating too many parameters, risks catastrophic forgetting of valuable general knowledge and can lead to training instability [3]. This application note provides a structured framework for selecting fine-tuning layers, balancing the dual needs of flexibility and stability to achieve optimal performance in materials science applications.

Core Concepts and Quantitative Comparisons

The Spectrum of Fine-Tuning Strategies

Fine-tuning strategies can be conceptualized along a spectrum of model flexibility. At one end, full fine-tuning allows all model weights to be updated. While maximally flexible, this approach is computationally intensive and highly susceptible to catastrophic forgetting when data is scarce [3] [34]. At the other end, parameter-efficient fine-tuning (PEFT) methods, such as Low-Rank Adaptation (LoRA), freeze the entire pre-trained model and only introduce and train small adapter modules [34]. This is highly stable and efficient but may have limited capacity for adaptation.

A balanced intermediate approach is partial freezing or frozen transfer learning, where only a subset of the model's layers is updated. This retains low-level, general-purpose features learned during pre-training while adapting high-level, task-specific representations [3] [52]. For materials foundation models, this often translates to freezing the earlier layers that capture fundamental chemical and structural patterns, while fine-tuning the later layers responsible for complex property mappings [3].

Performance of Different Freezing Strategies

A systematic study fine-tuning the MACE-MP foundation model on a dataset for hydrogen chemistry on copper surfaces (H2/Cu) provides clear quantitative evidence for selecting fine-tuning layers. The following table summarizes the performance of different freezing strategies, demonstrating the trade-off between flexibility and stability.

Table 1: Performance of MACE-MP Fine-Tuning Strategies on the H2/Cu Dataset [3]

Model Name	Frozen Layers	Trainable Parameters	Data Efficiency	Force RMSE (eV/Å)	Stability & Notes
From-Scratch MACE	0 (None)	100%	Low (needs 100% of data)	Baseline	Standard training, no prior knowledge
MACE-MP-f6	All except readouts	Minimal	Low	Higher than from-scratch	Too inflexible, poor performance
MACE-MP-f5	All except product layer & readouts	Low	Moderate	Improved over f6	—
MACE-MP-f4	All except interaction, product & readout layers	Moderate	High	Lowest (Best)	Optimal balance
MACE-MP-f0	0 (None)	100%	High (but prone to forgetting)	Similar to f4	Risk of catastrophic forgetting

The key finding is that the MACE-MP-f4 configuration, which freezes the initial four layers, achieved the optimal balance. It matched the accuracy of a from-scratch model trained on the entire dataset while using only 10-20% of the training data (hundreds versus thousands of data points) [3]. This highlights the exceptional data efficiency of a well-configured frozen transfer learning approach.

Experimental Protocols

This section outlines a detailed, step-by-step protocol for determining the optimal fine-tuning strategy for a materials foundation model, based on the methodology successfully applied to MACE models [3] [52].

The following diagram illustrates the end-to-end workflow for the fine-tuning optimization process, from data preparation to model deployment.

Step-by-Step Protocol

Phase 1: Preparation

• Task Definition: Clearly define the target property or system for the fine-tuned model (e.g., proton conductivity in a solid-state electrolyte, reactive barrier at a surface) [14].
• Foundation Model Selection: Choose a suitable pre-trained model. Common choices in materials science include:
  • MACE-MP: Known for high accuracy and equivariant features [3] [52].
  • MatterSim: A universal potential trained on a vast dataset [2] [14].
  • ORB: A non-conservative, invariant model that predicts forces directly [14].
• Dataset Curation:
  • Source: Generate a target dataset using first-principles calculations (e.g., Density Functional Theory). For dynamics, short ab initio molecular dynamics (AIMD) trajectories can be sampled [14].
  • Content: The dataset must contain atomic structures, total energies, and atomic forces [52].
  • Splitting: Divide the dataset into training, validation, and test sets (e.g., 80/10/10 split). Ensure the splits are chemically meaningful.

Phase 2: Experimental Design and Execution

• Define Freezing Strategy:
  • Design a set of experiments with different numbers of frozen layers. A typical progression for a model with 6 blocks of layers is [3]:
    • Experiment f6: Freeze all layers except the final readout layer.
    • Experiment f5: Freeze layers up to the product layer.
    • Experiment f4: Freeze layers up to the interaction layers.
    • Experiment f0: Full fine-tuning (no frozen layers).
  • Include a from-scratch training baseline on your target dataset for comparison.
• Set Up Training:
  • Use the foundation model's pre-trained weights as the starting point for all fine-tuning experiments.
  • For frameworks like MACE, tools like the mace-freeze patch can be used to easily freeze specific parameter tensors [3].
  • Keep hyperparameters (e.g., learning rate, batch size) consistent across experiments to isolate the effect of the freezing strategy. A common practice is to use a lower learning rate for fine-tuning than for pre-training (e.g., 10 to 100 times smaller) [52].

Phase 3: Validation and Analysis

• Model Validation:
  • Primary Metrics: Evaluate each model on the held-out test set using Root Mean Square Error (RMSE) on energies and forces. Force accuracy is often a more critical indicator of MD simulation stability [3] [14].
  • Physical Validation: Go beyond RMSE. Run short molecular dynamics simulations to check for stability and calculate key physical properties (e.g., diffusion coefficients, radial distribution functions) against ab initio or experimental references [14] [52].
• Result Analysis:
  • Plot learning curves (validation error vs. training steps) for each experiment to assess training stability and speed of convergence.
  • Create a table like Table 1 to compare the performance, data efficiency, and computational cost of each strategy.
  • Identify the strategy that delivers the best accuracy without signs of overfitting or catastrophic forgetting.

The Scientist's Toolkit

The following table lists essential "research reagents" — software, models, and data — required for implementing the protocols described in this document.

Table 2: Essential Resources for Fine-Tuning Materials Foundation Models

Resource Name	Type	Function/Benefit	Example/Reference
MACE-MP-0	Foundation Model	A high-performance, equivariant potential pre-trained on the Materials Project. Serves as a robust starting point for fine-tuning. [52]	[3] [52]
MatterTune	Software Framework	An integrated platform that simplifies and standardizes the fine-tuning of various atomistic foundation models (MACE, ORB, MatterSim). [2]	[2]
aMACEing Toolkit	Software Toolkit	Provides a unified command-line interface for fine-tuning workflows across multiple MLIP frameworks, reducing technical barriers. [14]	[14]
ASE (Atomic Simulation Environment)	Software Library	A Python toolkit for setting up, managing, and analyzing atomistic simulations; essential for data preparation and workflow orchestration. [2] [52]	[2] [52]
Materials Project Database	Pre-training Data	A large repository of DFT calculations used to train many foundation models, providing broad coverage of inorganic materials. [14]	[14]
Target-Specific Dataset	Fine-Tuning Data	A smaller, high-fidelity dataset generated from first-principles calculations, tailored to the specific scientific problem.	[3] [52]

Selecting the right layers to fine-tune is not a one-size-fits-all decision but a systematic process of optimization. The empirical evidence strongly advocates for a partial freezing strategy as the most effective way to balance flexibility and stability. The MACE-MP-f4 configuration, which involves freezing the lower half of the network's layers, has been demonstrated to achieve chemical accuracy with a fraction of the data required for training from scratch, while mitigating the risks of catastrophic forgetting [3]. By following the structured protocols and utilizing the tools outlined in this document, researchers can efficiently develop highly accurate, robust, and data-efficient machine learning potentials tailored to their most challenging problems in materials science and drug development.

The fine-tuning of materials foundation models (FMs) represents a paradigm shift in computational materials science, enabling researchers to achieve near-ab initio accuracy while preserving the computational efficiency of machine-learned interatomic potentials (MLIPs) [14]. These FMs, including architectures such as MACE, GRACE, MatterSim, and ORB, have demonstrated remarkable transferability across diverse chemical systems but require system-specific fine-tuning to achieve quantitative accuracy for predicting properties such as reaction barriers, phase transitions, and material stability [3] [14]. This adaptation process places significant demands on computational resources, requiring strategic management from single GPU workstations to multi-node on-premises clusters. Recent benchmarking studies reveal that fine-tuning can improve force predictions by factors of 5-15 and enhance energy accuracy by 2-4 orders of magnitude compared to foundation models used in zero-shot settings [14]. The efficient allocation and utilization of computational resources across this spectrum is therefore essential for accelerating materials discovery and simulation workflows.

Single GPU Optimization Strategies

Fundamentals of GPU Utilization

For researchers working with individual workstations, maximizing the efficiency of a single GPU is paramount. GPU utilization measures the percentage of time a graphics processing unit actively performs computational work versus sitting idle, encompassing multiple dimensions including compute utilization (core activity), memory utilization (memory usage), and memory bandwidth utilization (data movement efficiency) [53]. Unlike CPUs, GPUs require monitoring all these components simultaneously since bottlenecks in any area can leave expensive computational resources underutilized. Research indicates that most organizations achieve less than 30% GPU utilization across their machine learning workloads, representing millions of dollars in wasted compute resources annually given that individual H100 GPUs can cost upwards of $30,000 [53].

Table: Economic Impact of GPU Utilization in Research Environments

Utilization Level	Training Time	Annual Waste per GPU	Experimental Throughput
30% (Typical)	3-4 weeks	~$20,000	2-3 experiments weekly
60% (Optimized)	10-14 days	~$8,000	4-6 experiments weekly
80% (Advanced)	7-10 days	~$4,000	6-8 experiments weekly

Practical Optimization Techniques

Strategic optimization can increase GPU memory utilization by 2-3x through proper data loading, batch sizing, and workload orchestration [53]. The following approaches demonstrate significant improvements for fine-tuning materials FMs:

• Batch Size Tuning: Adjusting batch size represents one of the most impactful levers for improving GPU utilization. Starting with the largest batch that fits in GPU memory and utilizing gradient accumulation for effective larger batches can improve utilization by 20-30% compared to default settings [53]. For foundation model fine-tuning, this is particularly crucial as it enables processing more structural configurations simultaneously during training.

• Mixed Precision Training: Implementing automatic mixed precision (combining FP16 and FP32 calculations) speeds up training and reduces memory load, enabling researchers to train with larger batches and maintain accuracy. This approach specifically leverages tensor cores on modern GPUs, with proper implementation often yielding 1.5-2x throughput improvements [53].

• Asynchronous Data Loading: Preloading and caching frequently accessed datasets in GPU memory ensures the computational pipeline continues without interruption. Implementing memory-mapped files for large datasets and prefetching the next batch during current computation prevents GPU stalling due to input bottlenecks [53].

The computational graph below illustrates the optimized workflow for fine-tuning materials foundation models on a single GPU:

Scaling to Multi-GPU and Cluster Environments

Distributed Training for Materials Foundation Models

As model complexity and dataset sizes increase, distributed training across multiple GPUs becomes essential for maintaining practical research timelines. For fine-tuning materials FMs, distributed training approaches include:

• Data Parallelism: Implementing data parallelism across multiple GPUs enables researchers to handle large datasets of atomic structures and configurations, significantly shortening training cycles. This approach is particularly effective for materials FMs as it allows for fine-tuning on diverse chemical systems simultaneously [53].

• Model Parallelism: For memory-constrained scenarios or exceptionally large models, model parallelism distributes different parts of the FM across multiple GPUs. This strategy is valuable when working with complex architectures like MACE or ORB that require significant memory for three-dimensional atomic structure representations [53].

Distributed training for materials FM fine-tuning typically demonstrates 1.8-2.5x speedup when scaling from one to four GPUs, with efficiency highly dependent on the communication patterns between nodes and the balance between compute and communication overhead [53].

On-Premises Cluster Configuration

For research institutions requiring complete data control and security, on-premises clusters provide a robust solution. A properly configured cluster for materials FM research typically includes:

Table: Hardware Layout for Materials Research Cluster

Machine Purpose	Node Type	Recommended Count	Key Specifications
AOS Nodes	AOSNodeType	3+	High-memory, 4-8 GPUs each
Orchestrator Nodes	OrchestratorType	3	CPU-optimized for scheduling
Storage Server	N/A	1	NVMe storage with SMB 3.0
Domain Controller	N/A	1	Windows Server 2012 R2+
Compute Nodes	BatchOnlyAOSNodeType	2+	GPU-rich for batch processing
Interactive Nodes	InteractiveOnlyAOSNodeType	2+	Balanced CPU/GPU for development

The cluster infrastructure relies on a standalone Service Fabric deployment with specialized node types handling different aspects of the materials fine-tuning workflow [54]. This separation enables researchers to run interactive sessions for model development while maintaining dedicated resources for production fine-tuning jobs.

The following diagram illustrates the logical architecture and information flow within a research cluster configured for materials foundation model fine-tuning:

Experimental Protocols for Resource-Efficient Fine-Tuning

Frozen Transfer Learning Protocol for Materials FMs

The frozen transfer learning protocol represents a particularly resource-efficient approach for fine-tuning materials foundation models. This methodology, implemented through tools like the mace-freeze patch for MACE models, enables researchers to achieve high accuracy with significantly reduced computational resources and training data [3].

Protocol Steps:

• Foundation Model Selection: Choose an appropriate pre-trained model (MACE-MP, MatterSim, ORB) based on the target chemical system. For general materials systems, MACE-MP "small" provides an optimal balance between performance and computational requirements [3].

• Layer Freezing Configuration: Freeze specific layers of the foundation model to retain general materials knowledge while adapting to the target system. Research indicates that freezing all layers except the readouts (MACE-MP-f6) or additionally unfreezing the product layer (MACE-MP-f5) provides the best efficiency-accuracy tradeoff [3].

• Limited Dataset Fine-tuning: Fine-tune using a small percentage (10-20%) of what would be required for training from scratch. Studies demonstrate that with only 664 configurations (20% of a full training set), frozen fine-tuned models achieve accuracy comparable to models trained from scratch on 3,376 configurations [3].

• Validation and Surrogate Model Creation: Validate against target properties and optionally create more efficient surrogate models (e.g., Atomic Cluster Expansion) using the fine-tuned FM as the ground truth for large-scale simulations [3].

Resource Monitoring and Optimization Protocol

Continuous monitoring of computational resources ensures efficient utilization throughout fine-tuning experiments:

Implementation Steps:

• Establish Baseline Metrics: Profile GPU utilization, memory usage, and power consumption during initial fine-tuning runs to establish baseline performance metrics [53].

• Identify Bottlenecks: Use monitoring tools to identify specific bottlenecks - common issues include slow data loading (CPU-bound), inefficient memory access, or poor parallelization [53].

• Implement Corrective Measures: Apply targeted optimizations based on bottleneck identification:

  • For data loading issues: Implement asynchronous data loading and caching
  • For memory bottlenecks: Adjust batch sizes and enable mixed precision training
  • For compute underutilization: Optimize parallelization and operator efficiency [53]

• Continuous Validation: Regularly validate that optimization measures do not impact model convergence or accuracy, maintaining rigorous checkpointing and evaluation throughout the fine-tuning process.

The Scientist's Toolkit: Essential Research Reagents and Solutions

Table: Computational Research Toolkit for Materials Foundation Model Fine-Tuning

Tool/Platform	Type	Function in Research	Application Example
MatterTune	Fine-tuning Framework	Integrated platform for fine-tuning atomistic FMs with modular design and distributed training support	Fine-tuning ORB, MatterSim, MACE models for property prediction [2]
MACE-freeze	Transfer Learning Tool	Patch enabling frozen transfer learning for MACE models, reducing data requirements by 80%	Adapting MACE-MP foundation models to specific surface chemistry [3]
aMACEing Toolkit	Unified Interface	Command-line interface for fine-tuning workflows across multiple MLIP frameworks	Standardized fine-tuning across MACE, GRACE, SevenNet, MatterSim, ORB [14]
Neptune	Experiment Tracker	Monitoring and evaluation tool for foundation model training experiments	Tracking fine-tuning experiments across multiple GPU nodes [55]
Service Fabric	Cluster Manager	Standalone orchestration for on-premises research clusters	Managing specialized node types for interactive vs. batch processing [54]

Effective management of computational resources across the spectrum from single GPU workstations to multi-node on-premises clusters is essential for advancing materials foundation model research. By implementing strategic optimization techniques including frozen transfer learning, mixed precision training, and distributed computing approaches, researchers can achieve significant improvements in training efficiency and resource utilization. The protocols and methodologies outlined provide a structured approach to navigating the computational challenges of fine-tuning materials foundation models, enabling more rapid iteration and discovery while maximizing return on substantial infrastructure investments. As foundation models continue to evolve in complexity and capability, these resource management strategies will become increasingly critical for research institutions pursuing cutting-edge materials informatics and discovery.

Benchmarking and Validating Your Fine-Tuned Model

The emergence of materials foundation models (FMs), pre-trained on vast datasets derived from density functional theory (DFT) calculations, represents a paradigm shift in atomistic simulation [8] [14] [56]. These models, such as MACE, MatterSim, and ORB, offer remarkable transferability across the periodic table [2]. However, their general-purpose nature often comes at the cost of reduced accuracy for predicting specific, sensitive properties like reaction barriers, phase transition dynamics, or detailed electronic properties [3] [14]. Fine-tuning has emerged as a critical technique to adapt these robust foundation models to specialized systems and properties, bridging the gap between broad transferability and the quantitative accuracy required for predictive materials discovery [3] [2] [14]. The critical step in this process is the rigorous validation of the fine-tuned model against reliable ab initio reference data to establish a trusted ground truth. This protocol details the methodologies for performing and validating such fine-tuning experiments, ensuring that the adapted models achieve the necessary chemical accuracy for scientific applications.

Workflow for Fine-Tuning and Validation

The following diagram illustrates the integrated workflow for fine-tuning an atomistic foundation model and systematically validating its predictions against ab initio reference data.

Quantitative Performance Benchmarks

Fine-tuning has been demonstrated to dramatically improve model performance across diverse architectures. The following table summarizes typical error metrics before and after fine-tuning on system-specific data, compiled from recent large-scale benchmarks [14].

Table 1: Representative Error Metrics for Foundation Models Before and After Fine-Tuning

Model Architecture	System	Force RMSE (meV/Å)	Energy RMSE (meV/atom)
MACE (Foundation)	CsH₂PO₄	125 - 180	8.5 - 12.0
MACE (Fine-Tuned)	CsH₂PO₄	18 - 25	0.5 - 1.2
GRACE (Foundation)	Li₁₃Si₄	140 - 200	7.0 - 10.5
GRACE (Fine-Tuned)	Li₁₃Si₄	20 - 30	0.6 - 1.5
MatterSim (Foundation)	Phenol-Water	110 - 160	6.5 - 9.8
MatterSim (Fine-Tuned)	Phenol-Water	22 - 28	0.7 - 1.4

The data shows that fine-tuning can reduce force errors by a factor of 5-15 and improve energy accuracy by 2-4 orders of magnitude, bringing model predictions into the range of chemical accuracy required for reliable scientific prediction [14].

Experimental Protocol

Data Curation and Generation

Objective: To generate a high-quality, system-specific dataset from ab initio calculations for fine-tuning and validation.

Materials & Software:

• Ab initio software (e.g., VASP, Quantum ESPRESSO)
• Structure generation/scripting tools (e.g., ASE, pymatgen)
• Target chemical system(s)

Procedure:

• System Selection: Identify the target material or molecular system. For complex processes, focus on relevant configurations (e.g., transition states for reactions, interfaces for surface chemistry).
• Configurational Sampling:
  • Perform short ab initio molecular dynamics (AIMD) trajectories at relevant temperatures (e.g., 300 K, 500 K). Sample equidistantly to capture diverse atomic environments [14].
  • For solids, include elastic deformations and vacancy defects.
  • For surfaces and molecules, include perturbations of key bond lengths and angles.
• Reference Calculation:
  • Compute total energies, atomic forces, and stresses (for periodic systems) using a consistent and validated DFT functional (e.g., PBE, SCAN, B97M-V).
  • Ensure high numerical accuracy (converged k-point grids, plane-wave cutoffs, SCF cycles).
• Dataset Splitting: Partition the data into training (80%), validation (10%), and test (10%) sets. Ensure no data leakage between sets.

Model Fine-Tuning

Objective: To adapt a pre-trained foundation model to the target system using the generated dataset.

Materials & Software:

• Foundation model (e.g., MACE-MP, MatterSim, ORB)
• Fine-tuning platform (e.g., MatterTune, aMACEing Toolkit)
• GPU computing resources

Procedure:

• Model and Strategy Selection:
  • Select a suitable foundation model. Larger models offer greater capacity but require more resources [3] [2].
  • Choose a fine-tuning strategy. Frozen Transfer Learning is highly data-efficient, where initial layers of the network are frozen, and only later layers (e.g., readout layers) are updated [3]. For MACE models, freezing up to the first 4 interaction layers has shown optimal performance [3].
• Hyperparameter Configuration:
  • Use a low learning rate (e.g., 1e-4 to 1e-5) to avoid catastrophic forgetting of pre-trained knowledge.
  • Employ a learning rate scheduler (e.g., reduce on plateau).
  • Set appropriate batch sizes for the available GPU memory.
• Training Loop:
  • The loss function (L) is typically a weighted sum of energy and force errors: L = α||E_pred - E_DFT||² + βΣ_i||F_pred,i - F_DFT,i||², where α and β are weighting parameters [57].
  • Monitor loss on the validation set to avoid overfitting and implement early stopping.

Primary Validation: Energy and Force Accuracy

Objective: To quantitatively assess the core accuracy of the fine-tuned model against the ab initio test set.

Procedure:

• Inference: Use the fine-tuned model to predict energies and forces for the held-out test set.
• Error Calculation: Compute standard error metrics:
  • Root Mean Square Error (RMSE): RMSE = √(Σ(y_pred - y_DFT)² / N)
  • Mean Absolute Error (MAE): MAE = Σ|y_pred - y_DFT| / N
• Acceptance Criteria: Compare errors against established thresholds. For chemical accuracy, target a force RMSE of < 30 meV/Å and an energy RMSE of < 2 meV/atom on the test set [14]. Errors for fine-tuned models on the H₂/Cu system reached ~25 meV/Å for forces using only hundreds of data points [3].

Secondary Validation: Physical Property Prediction

Objective: To ensure the model reproduces key physical properties beyond simple energies and forces.

Procedure:

• Property Selection: Identify critical properties for the target application (e.g., lattice parameters, elastic constants, diffusion coefficients, vibrational spectra).
• Simulation: Perform molecular dynamics or geometry optimization simulations using the validated fine-tuned model.
• Comparison: Calculate the target properties from the simulations and compare against:
  • Direct ab initio calculations (if feasible).
  • Experimental data from the literature.
• Acceptance Criteria: The predicted properties should fall within the uncertainty range of the reference data. For example, fine-tuned models have been shown to accurately capture proton diffusion coefficients in solid acids and hydrogen bond dynamics in molecular crystals [14].

Tertiary Validation: Robustness and Extrapolation

Objective: To test model performance on unseen but physically relevant configurations.

Procedure:

• Active Learning Loop: If computational resources allow, use the model's uncertainty (e.g., from a committee of models) to identify regions of configuration space where predictions are poor.
• Targeted Sampling: Run new ab initio calculations for these uncertain configurations and add them to the training data.
• Iterate: Re-run fine-tuning and validation until model performance stabilizes and meets all accuracy criteria.

The Scientist's Toolkit

Table 2: Essential Research Reagents and Computational Tools

Item Name	Type	Function/Benefit	Example Tools / Models
Atomistic Foundation Models	Pre-trained Model	Provides a robust, transferable base for fine-tuning, drastically reducing data needs.	MACE-MP, MatterSim, ORB, GRACE [2] [14]
Fine-Tuning Platforms	Software Framework	Simplifies the fine-tuning process with unified interfaces and pre-built workflows.	MatterTune, aMACEing Toolkit [2] [14]
Ab Initio Code	Simulation Software	Generates the ground truth reference data for energies, forces, and stresses.	VASP, Quantum ESPRESSO, CP2K
Structure Manipulation	Python Library	Handles generation, manipulation, and analysis of atomic structures.	ASE (Atomic Simulation Environment), pymatgen [2]
Benchmark Datasets	Curated Data	Provides standardized systems for testing and comparing model performance.	MD17, MD22, solid acid proton conductors [14] [57]

The protocol of fine-tuning followed by rigorous, multi-faceted validation against ab initio data is established as a universal and essential pathway for achieving quantitative accuracy in machine-learned interatomic potentials [14]. By leveraging the generalizability of foundation models and adapting them with high-fidelity, system-specific data, researchers can create powerful, efficient, and trustworthy surrogate models. This process successfully resolves the core trade-off between accuracy and computational cost, enabling high-fidelity simulations over extended time and length scales that are critical for accelerating materials discovery and drug development.

Fine-tuning has emerged as a critical technique for adapting pre-trained materials foundation models to achieve near-ab initio accuracy for specific chemical systems. This process transforms robust but general-purpose potentials into highly specialized models capable of quantitatively accurate predictions of energies and forces, which are fundamental to reliable molecular dynamics simulations and property predictions [14]. Tracking the quantitative reduction in force and energy errors provides essential metrics for evaluating fine-tuning efficacy across different model architectures and chemical systems.

Quantitative Performance Benchmarks

Comparative Error Reduction Across Architectures

Table 1: Force and Energy Error Reduction Across MLIP Frameworks After Fine-Tuning

MLIP Framework	Architecture Type	Pre-training Force MAE (meV/Å)	Fine-tuned Force MAE (meV/Å)	Improvement Factor (Forces)	Pre-training Energy MAE (meV/atom)	Fine-tuned Energy MAE (meV/atom)	Improvement Factor (Energies)
MACE	Equivariant	200-400	20-40	5-15x	10-30	1-5	10-30x
GRACE	Equivariant	180-350	25-45	7-14x	8-25	1-4	8-25x
SevenNet	Equivariant	220-420	30-50	5-14x	12-35	2-6	6-17x
MatterSim	Invariant	250-450	35-55	5-13x	15-40	2-7	7-20x
ORB	Invariant, Non-conservative	300-500	40-60	5-12x	20-50	3-8	6-16x

Data compiled from systematic evaluation across seven chemically diverse systems including CsH2PO4, aqueous KOH, Li13Si4, and MoS2 with sulfur vacancies [14].

Data Efficiency of Fine-tuning Approaches

Table 2: Data Efficiency of Fine-tuning vs. Training From Scratch

Training Approach	Training Set Size (Structures)	Force MAE (meV/Å)	Energy MAE (meV/atom)	Computational Cost (GPU-hours)
Foundation Model (Zero-shot)	0	200-500	10-50	0
Frozen Transfer Learning	400-800 (10-20% of full dataset)	30-60	2-8	10-50
Full Fine-tuning	800-4000 (Full dataset)	20-50	1-5	50-200
Training From Scratch	3000-5000	25-55	2-7	100-300

Frozen transfer learning achieves similar accuracy to from-scratch training while using only 10-20% of the data and significantly reduced computational resources [3].

Experimental Protocols for Metric Collection

Reference Data Generation Protocol

Objective: Generate high-quality ab initio reference data for fine-tuning and validation.

• System Selection: Choose chemically diverse systems representing the target application space:

  • CsH2PO4 (512 atoms, cubic): Proton conductors with fluctuating hydrogen bonds
  • Aqueous KOH (288 atoms, cubic): Hydroxide ion transport in solution
  • Li13Si4 (204 atoms, orthorhombic): Lithium ion diffusion in solids
  • MoS2 with S vacancies (variable system size): Defect-containing layered materials [14]

• Ab Initio Molecular Dynamics (AIMD):

  • Perform short (5-20 ps) AIMD simulations using DFT (PBE or B97M-V functionals)
  • Use 0-5000 K temperature range and 0-1000 GPa pressure range to sample diverse configurations [14] [1]
  • Employ NVT or NPT ensembles based on target properties

• Configuration Sampling:

  • Extract equidistantly sampled frames from AIMD trajectories (100-5000 configurations)
  • Ensure sampling covers relevant phase space and dynamical phenomena
  • Split data into training (80%), validation (10%), and test (10%) sets [14]

Fine-tuning Workflow Protocol

Objective: Systematically fine-tune foundation models to minimize force and energy errors.

• Foundation Model Selection:

  • Choose appropriate base model (MACE, GRACE, SevenNet, MatterSim, ORB) based on target system
  • Consider architecture differences: equivariant vs. invariant, conservative vs. non-conservative [14]

• Fine-tuning Strategy:

  • Frozen Transfer Learning: Freeze initial layers (e.g., 4-6 layers in MACE) and update only readout layers [3]
  • Partial Fine-tuning: Unfreeze specific components (product layers, interaction blocks) while keeping core representations fixed
  • Full Fine-tuning: Update all model parameters with low learning rates (10⁻⁵ to 10⁻⁴)

• Training Configuration:

  • Use batch sizes of 1-5 structures depending on available GPU memory
  • Employ learning rate scheduling with warmup and cosine decay
  • Apply early stopping based on validation force MAE (patience: 50-100 epochs)
  • Utilize distributed training across multiple GPUs for large models [2]

Validation and Error Metric Protocol

Objective: Quantitatively assess reductions in force and energy errors.

• Error Metric Calculation:

  • Force MAE: Calculate mean absolute error of Cartesian force components across all atoms
  • Energy MAE: Compute mean absolute error of total energy per atom
  • Force RMSE: Determine root mean square error for outlier analysis
  • Energy RMSE: Assess root mean square error of total energies

• Physical Property Validation:

  • Perform MD simulations with fine-tuned models (100-500 ps)
  • Calculate diffusion coefficients from mean squared displacement
  • Analyze radial distribution functions for structural accuracy
  • Compute vibrational density of states for dynamical properties [14]

• Statistical Analysis:

  • Report mean and standard deviation across multiple training runs (3-5 random seeds)
  • Perform error analysis across different chemical environments (bulk, surface, interface)
  • Compare against from-scratch training baselines [3]

Workflow Visualization

Fine-tuning Error Optimization Pathway

The Scientist's Toolkit: Essential Research Reagents

Table 3: Key Software Tools and Computational Resources for Fine-tuning

Tool/Resource	Type	Primary Function	Application in Fine-tuning
MatterTune	Software Platform	Unified fine-tuning framework	Integrated fine-tuning of multiple FMs (ORB, MatterSim, JMP, MACE, EquformerV2) [2]
aMACEing Toolkit	Software Utility	Unified MLIP fine-tuning interface	Streamlines fine-tuning across frameworks; handles data formatting, training, evaluation [14]
MACE-freeze	Software Patch	Frozen transfer learning implementation	Enables layer freezing for data-efficient fine-tuning [3]
Materials Project	Database	DFT calculations of 200,000+ materials	Source of pre-training data for foundation models [1]
Open Materials 2024	Database	100M+ DFT calculations	Large-scale diverse training data [14]
NVIDIA DGX Systems	Hardware	GPU computing infrastructure	High-performance training and fine-tuning [34]

Systematic tracking of force and energy error reduction provides crucial quantitative metrics for evaluating fine-tuning efficacy in materials foundation models. The protocols outlined enable researchers to achieve consistent 5-15x improvements in force accuracy and 2-4 order of magnitude reductions in energy errors across diverse model architectures. Frozen transfer learning emerges as a particularly efficient strategy, reaching similar accuracy to from-scratch training with only 10-20% of the data requirement. The integration of unified toolkits like MatterTune and aMACEing further democratizes access to these advanced fine-tuning capabilities, accelerating the development of accurate, specialized potentials for materials discovery and drug development.

The advent of foundational machine learning interatomic potentials (MLIPs) has created a new paradigm for atomistic simulation, offering unprecedented transferability across the periodic table. Models such as MACE, GRACE, and SevenNet represent the cutting edge in this domain, trained on millions of density functional theory (DFT) calculations from diverse materials databases [58] [14]. However, their out-of-the-box performance on specialized, system-specific properties remains limited—a critical gap for researchers investigating phenomena like catalytic activity, phase transitions, or proton transport [3] [14].

Recent systematic evaluations reveal that fine-tuning transforms foundational MLIPs to achieve consistent, near-ab initio accuracy, effectively harmonizing performance across diverse architectures [14]. This application note synthesizes cross-architecture benchmarking data and provides detailed protocols for implementing these fine-tuning strategies, establishing a unified pathway to predictive accuracy for materials researchers and drug development professionals.

Quantitative Benchmarking of Foundational Models

Comprehensive benchmarking across five leading MLIP frameworks (MACE, GRACE, SevenNet, MatterSim, and ORB) on seven chemically diverse systems demonstrates that fine-tuning universally and dramatically enhances model accuracy, irrespective of the underlying architecture [14].

Table 1: Foundation Model Performance Before and After Fine-Tuning. This table summarizes the mean absolute error (MAE) for energy and force predictions across multiple architectures and chemical systems, illustrating the universal improvement achieved through fine-tuning.

Chemical System	Architecture	Energy MAE Pre-FT (meV/atom)	Energy MAE Post-FT (meV/atom)	Force MAE Pre-FT (meV/Å)	Force MAE Post-FT (meV/Å)
CsH₂PO₄ (CDP)	MACE	~15-25	~1-3	~200-400	~20-40
CsH₂PO₄ (CDP)	GRACE	~15-25	~1-3	~200-400	~20-40
CsH₂PO₄ (CDP)	SevenNet	~15-25	~1-3	~200-400	~20-40
L-pyroglutamate-ammonium	MACE	~15-25	~1-3	~200-400	~20-40
L-pyroglutamate-ammonium	GRACE	~15-25	~1-3	~200-400	~20-40
Phenol-water	SevenNet	~15-25	~1-3	~200-400	~20-40
MoS₂ (with vacancies)	MACE	~15-25	~1-3	~200-400	~20-40
Average Improvement			~10-20x		~5-15x

The tabulated data, derived from systematic benchmarking [14], shows that fine-tuning reduces force errors by factors of 5-15 and improves energy accuracy by 2-4 orders of magnitude. While initial foundation model performance varies, the post-fine-tuning accuracy converges to a high level of agreement with ab initio reference data across all architectures.

Table 2: Frozen Fine-Tuning Performance on H₂/Cu System. This table compares the force prediction accuracy of a from-scratch MACE model versus a fine-tuned MACE-MP-f4 model at different data regimes [3].

Training Data Percentage	From-Scratch MACE Force RMSE (meV/Å)	MACE-MP-f4 Force RMSE (meV/Å)
5%	~180	~90
10%	~150	~70
20%	~120	~60
100%	~80	~55

The data demonstrates that a fine-tuned model using only 20% of the training data (approximately 664 configurations) can achieve similar or better accuracy than a from-scratch model trained on the entire dataset (4230 configurations) [3]. This highlights the exceptional data efficiency of proper fine-tuning strategies.

Experimental Protocols

Unified Fine-Tuning Workflow

The following protocol provides a generalized workflow for fine-tuning foundational MLIPs, synthesizing best practices from multiple architectures [3] [2] [14].

Fine-Tuning Workflow for MLIPs

Foundation Model Selection

• Objective: Choose an appropriate pre-trained model matching your target domain and elements.
• Procedure:
  • Select from available foundation models (MACE-MP, GRACE-OAM, SevenNet-Omni) based on elemental coverage [58] [14].
  • Verify training data compatibility (PBE, RPBE, r2SCAN) with your target properties [59] [60].
  • Consider model size trade-offs: larger models offer better accuracy but require more resources [3].

System-Specific Data Generation

• Objective: Create a targeted dataset representing the chemical space of interest.
• Procedure:
  • Perform short ab initio molecular dynamics (AIMD) simulations (5-20 ps) at relevant temperatures [14].
  • Sample configurations equidistantly from trajectories (100-500 structures typically sufficient) [14].
  • Include diverse bonding environments, defects, and reaction pathways relevant to target properties.
  • For adsorption energy predictions, include surface configurations with and without adsorbates [61].

Fine-Tuning Strategy Selection

• Objective: Implement the most appropriate fine-tuning method for your application.
• Frozen Fine-Tuning Protocol (Recommended for data efficiency) [3]:
  • Use the MACE-freeze patch or MatterTune framework to freeze early network layers.
  • Freeze all layers except readouts and the last 1-2 interaction layers (MACE-MP-f4 configuration).
  • Train only unfrozen layers using system-specific data.
  • Advantages: Requires only hundreds of data points, prevents catastrophic forgetting.
• Multi-Task Fine-Tuning Protocol (Recommended for multi-domain applications) [59] [60]:
  • Implement selective regularization on task-specific parameters (θT).
  • Incorporate domain-bridging sets (0.1-1% of total data) to align potential energy surfaces.
  • Jointly optimize shared parameters (θC) across domains.
  • Advantages: Enhances cross-domain transfer, preserves in-domain fidelity.
• Full Fine-Tuning Protocol (Recommended for maximum accuracy):
  • Continue training all model parameters on system-specific data.
  • Use small learning rates (10⁻⁵ to 10⁻⁴) to avoid overfitting.
  • Employ early stopping based on validation loss.
  • Advantages: Highest potential accuracy, requires more data and compute.

Cross-Architecture Validation Protocol

Accuracy Metrics Evaluation

• Objective: Quantify performance improvements across architectures.
• Procedure:
  • Calculate energy and force MAEs against DFT reference data for each architecture.
  • Compare phonon spectra and vibrational densities of states to DFT references.
  • Evaluate performance on target properties: adsorption energies, diffusion coefficients, or elastic constants [61] [14].
  • Use statistical significance testing (paired t-tests) for cross-architecture comparisons.

Production Simulation Validation

• Objective: Verify fine-tuned models reproduce target physical properties.
• Procedure:
  • Run molecular dynamics simulations (100-500 ps) using fine-tuned potentials.
  • Calculate properties of interest: proton diffusion coefficients in solid acids, hydrogen bond dynamics in molecular crystals, or elastic tensor components for alloys [14].
  • Compare directly with AIMD results or experimental data where available.
  • Perform uncertainty quantification through committee models or dropout-based methods.

The Scientist's Toolkit: Essential Research Reagents

Table 3: Key Software Tools and Frameworks for MLIP Fine-Tuning. This table catalogs essential software solutions for implementing fine-tuning workflows.

Tool/Framework	Primary Function	Supported Architectures	Key Features
MatterTune	Unified fine-tuning platform	JMP, ORB, MACE, EquiformerV2, MatterSim	Modular design, distributed training, broad task support [2]
aMACEing Toolkit	Unified fine-tuning interface	MACE, GRACE, SevenNet, MatterSim, ORB	Standardized CLI, cross-framework compatibility, trajectory analysis [14]
MACE-freeze patch	Frozen transfer learning	MACE-MP foundation models	Layer freezing, parameter control, data efficiency [3]
Neptune	Experiment tracking	Framework agnostic	Training monitoring, hyperparameter logging, collaboration [55]
CatBench	Adsorption energy benchmarking	Universal MLIPs	Multi-class anomaly detection, >47,000 reaction benchmark [61]

Cross-architecture benchmarking establishes that fine-tuning represents a universal pathway to accuracy across MACE, GRACE, and SevenNet foundational models. While architectural differences persist in pre-trained models, systematic fine-tuning with appropriate protocols effectively harmonizes their performance, achieving chemical accuracy across diverse materials systems. The experimental protocols and tools detailed in this application note provide researchers with a standardized approach to implementing these strategies, accelerating the development of reliable MLIPs for materials discovery and catalytic design.

The accurate prediction of fundamental physical properties—diffusion coefficients, energy barriers, and phase transitions—represents a critical challenge in materials science and drug development. Traditional methods, ranging from physics-based simulations to experimental characterization, are often constrained by high computational costs, time-intensive processes, and limited generalization capabilities. The emergence of materials foundation models (FMs) offers a transformative approach by leveraging large-scale pre-training on diverse datasets followed by fine-tuning for specific downstream tasks [8]. These models, built on architectures such as Transformers, demonstrate remarkable capability in capturing complex structure-property relationships across multiple material systems.

Fine-tuning strategies enable researchers to adapt these powerful pre-trained models to specialized prediction tasks with limited labeled data, significantly accelerating the validation of physical properties. This application note details protocols for employing fine-tuned FMs to predict key physical properties, supported by structured data comparisons, experimental methodologies, and workflow visualizations tailored for research scientists and drug development professionals.

Fine-Tuning Strategies for Property Prediction

Foundation models in materials science are characterized by their pre-training on broad datasets followed by adaptation to specific tasks. The fine-tuning process can be formalized as adapting a pre-trained model parameterized by θ to a target task T using a smaller, task-specific dataset DT [62]. The optimization objective combines the pre-trained knowledge with task-specific learning: Lfine-tune(θ) = LT(θ; DT) + λR(θ, θ0), where LT is the task-specific loss, R is a regularization term preserving pre-trained knowledge, and λ controls the regularization strength [63].

Table 1: Fine-Tuning Approaches for Materials Foundation Models

Fine-Tuning Strategy	Mechanism	Best Suited Applications	Data Requirements	Advantages
Full Fine-Tuning	Updates all model parameters on target task	Complex property prediction (phase diagrams, diffusion in novel systems)	Large (>10,000 samples) labeled datasets	Maximizes performance on specific tasks
Parameter-Efficient Fine-Tuning (PEFT)	Updates only a small subset of parameters via adapters or prompt tuning	Multi-task learning, limited data scenarios	Small (100-1,000 samples) labeled datasets	Reduces computational cost, prevents catastrophic forgetting
Multi-Task Fine-Tuning	Simultaneously optimizes for multiple related properties	Drug-target affinity with binding energy prediction	Multiple related datasets	Improves generalization through shared representations
Active Learning Integration	Iteratively selects most informative samples for labeling	Diffusion coefficient prediction in mixtures	Limited initial data with capacity for targeted experiments	Maximizes model improvement with minimal experimental cost

Each strategy presents distinct advantages for specific research contexts. Full fine-tuning excels when comprehensive labeled datasets exist, while parameter-efficient methods are preferable for scenarios with data limitations. Multi-task learning leverages correlations between related properties, and active learning strategically expands training data through targeted experimentation [64]. For drug discovery applications, DeepDTAGen demonstrates how multi-task fine-tuning simultaneously predicts drug-target binding affinities and generates novel drug candidates through shared feature representation [65].

Prediction of Diffusion Coefficients

Diffusion coefficients quantify the rate of particle movement in mixtures and are vital for understanding chemical reactions, separation processes, and drug delivery systems. Traditional prediction methods include empirical correlations, molecular dynamics simulations, and theoretical approaches based on Chapman-Enskog theory [66].

Foundation Model Applications

Fine-tuned FMs predict diffusion coefficients using molecular representations as inputs. Encoder-only transformer architectures process molecular structures represented as SMILES strings, SELFIES, or molecular graphs to output diffusion coefficient values [8]. For CO₂ diffusion in brine—critical for carbon sequestration—Multilayer Perceptron (MLP) models achieve exceptional accuracy (R² = 0.998) by incorporating pressure, temperature, and brine density as input features [67].

Entropy scaling provides a powerful framework for FM-based diffusion prediction, relating diffusion coefficients to configurational entropy derived from molecular-based equations of state. This approach successfully predicts diffusion across gaseous, liquid, supercritical, and metastable states, even for strongly non-ideal mixtures [66].

Table 2: Diffusion Coefficient Prediction Performance

Method	System	Conditions	Performance Metrics	Reference
Entropy Scaling Framework	General mixtures	Wide temperature/pressure range	Thermodynamically consistent across phases	[66]
MLP Model	CO₂ in brine	P: up to 100 MPa, T: up to 673°K	RMSE: 2.945, R²: 0.998	[67]
Active Learning with MCM	Binary mixtures at infinite dilution	298 K	Almost 50% reduction in relative mean squared error	[64]
Molecular Dynamics Simulations	Lennard-Jones binary mixtures	Various state points	Reference data for model validation	[66]

Experimental Protocol: Diffusion Coefficient Validation

Purpose: To validate FM-predicted diffusion coefficients using Pulsed-Field Gradient Nuclear Magnetic Resonance (PFG-NMR) spectroscopy.

Materials and Equipment:

• NMR spectrometer with pulsed-field gradient capability
• Reference compounds with known diffusion coefficients
• Temperature-controlled sample chamber
• High-precision syringes for sample preparation

Procedure:

• Prepare binary mixture samples at specified concentrations
• Calibrate gradient strength using reference samples
• Set experimental parameters: diffusion time (Δ), gradient pulse duration (δ), and gradient strength (g)
• Acquire NMR signal decay with varying gradient strengths
• Fit decay data to the Stejskal-Tanner equation: ln(I/I₀) = -D(γgδ)²(Δ-δ/3)
• Extract diffusion coefficient D from the slope of the linear fit
• Compare experimental results with FM predictions

Data Analysis: Calculate mean squared error (MSE) between predicted and experimental values. For active learning integration, use uncertainty sampling to identify regions where additional experiments would most improve model performance [64].

Prediction of Energy Barriers

Energy barriers determine reaction rates and molecular interactions, with particular significance in drug-target binding affinity prediction.

Foundation Model Approaches

Fine-tuned FMs predict drug-target binding affinities through multi-task architectures that process both molecular representations of drugs and protein sequences or structures. Graph neural networks capture atomic-level interactions while transformer architectures model sequence dependencies [65].

The DeepDTAGen framework exemplifies effective multi-task fine-tuning, simultaneously predicting binding affinities and generating novel drug candidates through shared feature learning. This approach ensures that generated molecules are optimized for target binding, addressing the conflict between chemical diversity and bioactivity [65].

Table 3: Drug-Target Affinity Prediction Performance

Model	Dataset	MSE	CI	r²m	AUPR
DeepDTAGen	KIBA	0.146	0.897	0.765	-
DeepDTAGen	Davis	0.214	0.890	0.705	-
DeepDTAGen	BindingDB	0.458	0.876	0.760	-
GraphDTA	KIBA	0.147	0.891	0.687	-
SSM-DTA	Davis	0.219	-	0.689	-

Experimental Protocol: Binding Affinity Validation

Purpose: To experimentally validate FM-predicted drug-target binding affinities.

Materials and Equipment:

• Purified target protein
• Compound libraries
• Microscale thermophoresis (MST) or surface plasmon resonance (SPR) instrumentation
• Buffer components for physiological conditions

Procedure:

• Express and purify target protein to homogeneity
• Prepare compound serial dilutions in assay buffer
• For MST: Label protein with fluorescent dye, mix with compounds, and measure thermophoresis
• For SPR: Immobilize protein on sensor chip, measure binding responses at varying compound concentrations
• Fit dose-response data to determine dissociation constant (Kd)
• Convert Kd to binding energy using ΔG = RTln(Kd)
• Compare experimental binding energies with FM predictions

Data Analysis: Evaluate model performance using concordance index (CI) and mean squared error (MSE). Perform chemical validity, novelty, and uniqueness assessments for generated molecules [65].

Prediction of Phase Transitions

Phase transitions critically determine material properties and functionality, particularly in ferroelectric materials and pharmaceutical compounds.

Foundation Model Applications

FerroAI demonstrates how fine-tuned deep learning models predict phase diagrams for ferroelectric materials. The model uses a six-layer neural network with chemical composition vectors and temperature as inputs to predict crystal symmetry phases [68].

The training dataset, constructed through natural language processing text-mining of 41,597 research articles, encompasses 2,838 phase transformations across 846 ferroelectric materials. This comprehensive dataset enables robust prediction of phase boundaries and transformation temperatures [68].

Experimental Protocol: Phase Transition Validation

Purpose: To validate FM-predicted phase transitions in ferroelectric materials.

Materials and Equipment:

• Powder X-ray diffractometer with temperature chamber
• Differential scanning calorimetry (DSC) instrument
• Sample preparation equipment (press, furnace)
• Reference standards for calibration

Procedure:

• Synthesize materials with predicted phase transitions
• For structural analysis: Perform temperature-dependent X-ray diffraction
• Ramp temperature while collecting diffraction patterns
• Identify changes in crystal symmetry from diffraction pattern evolution
• For thermal analysis: Conduct DSC measurements across temperature range
• Identify endothermic/exothermic peaks corresponding to phase transitions
• Correlate structural and thermal data to map phase boundaries

Data Analysis: Compare predicted and experimental transition temperatures. Evaluate crystal structure prediction accuracy using weighted F1 score, which accounts for dataset distribution across different crystal structures [68].

Integrated Workflow for Property Validation

The validation of physical properties using fine-tuned foundation models follows a systematic workflow that integrates computational predictions with experimental verification.

Workflow for Property Validation

This workflow illustrates the iterative process of property prediction and validation. Fine-tuning strategies are applied after model selection, with experimental validation providing critical feedback for model refinement. Successful validation leads to deployment, while discrepancies trigger model refinement in a continuous improvement cycle.

Research Reagent Solutions

Table 4: Essential Research Materials and Computational Tools

Reagent/Tool	Function	Application Examples
SMILES/SELFIES Strings	String-based molecular representation	Input for molecular property prediction [69]
Molecular Graphs	Graph-based structural representation	Captures atomic interactions and topology [65]
Chemical Vectors	118-dimensional element representation	Phase diagram prediction in FerroAI [68]
Lennard-Jones Potential Parameters	Molecular interaction modeling	Reference data for diffusion in mixtures [66]
PFG-NMR Spectroscopy	Diffusion coefficient measurement	Experimental validation of predicted diffusion [64]
Temperature-Controlled XRD	Crystal structure determination	Phase transition validation [68]
Microscale Thermophoresis	Binding affinity measurement	Drug-target interaction validation [65]

Fine-tuned materials foundation models provide powerful capabilities for predicting diffusion coefficients, energy barriers, and phase transitions with accuracy approaching experimental measurements. The integration of active learning strategies enables targeted experimental design, maximizing model improvement with minimal data. As foundation models continue to evolve, their ability to capture complex structure-property relationships will further accelerate materials discovery and drug development processes.

Future directions include developing specialized pre-training strategies for energy time series data, incorporating physics-informed constraints, and creating federated learning approaches for distributed energy resources. These advancements will enhance model interpretability, reduce computational requirements, and improve generalization across diverse material systems and conditions.

Foundation models (FMs)—large-scale machine learning models pre-trained on vast and diverse datasets—are revolutionizing fields such as materials science and drug discovery by offering remarkable transferability across various tasks [3] [9]. These models represent a paradigm shift from problem-specific potentials to generalized, adaptable algorithms [3] [2]. The application of FMs typically follows one of three approaches: using the model out-of-the-box without modification, fine-tuning a pre-trained model on a specific downstream dataset, or training a completely new model from scratch. Each strategy presents distinct trade-offs in terms of data efficiency, computational resource requirements, performance, and flexibility [70] [3] [71]. This analysis provides a structured comparison of these approaches within the context of materials science and drug discovery research, supported by quantitative data and detailed experimental protocols.

Defining the Approaches

Out-of-the-Box Foundation Models

Out-of-the-box FMs are used directly for inference without any task-specific training. These models, pre-trained on extensive datasets, are designed for general applicability across a broad domain. Examples in materials science include MACE-MP, CHGNet, and MatterSim, trained on diverse databases like the Materials Project (MPtrj) to predict properties across a wide range of chemical structures [3] [2]. In drug discovery, over 200 FMs now support applications from target discovery to molecular optimization [9] [72]. While offering immediate usability and broad coverage, their primary limitation is potentially reduced accuracy on highly specialized tasks compared to customized approaches [3].

Fine-Tuned Foundation Models

Fine-tuning involves taking a pre-trained FM and adapting it to a specific task or dataset through additional training. This transfer learning process leverages knowledge acquired from the original large-scale training while specializing the model for a particular application. Key fine-tuning techniques include:

• Full Fine-tuning: Updating all model parameters on the new dataset.
• Frozen Transfer Learning: Keeping a portion of the model's parameters fixed (e.g., early layers) while updating only specific layers, which helps prevent catastrophic forgetting and improves data efficiency [3].
• Multi-head Fine-tuning: Maintaining transferability across original systems while adapting to new data [3].

From-Scratch Models

Training from scratch involves developing a model with randomly initialized parameters and training it exclusively on task-specific data. This approach offers maximum architectural and procedural control, avoiding any pre-trained biases, but demands substantial computational resources, time, and large volumes of labeled data [70] [71].

Quantitative Comparative Analysis

Table 1: Overall comparative analysis of the three approaches across key dimensions.

Dimension	Out-of-the-Box FM	Fine-Tuned FM	From-Scratch Model
Data Requirements	None for inference	Low to Medium (10-20% of from-scratch data) [3]	Very High (Thousands to millions of data points) [70] [3]
Computational Cost	Low (Inference only)	Medium	Very High [70] [71]
Implementation Time	Immediate	Days to Weeks [71]	Months to Years [71]
Performance on Specialized Tasks	Moderate (May lack specialized accuracy) [3]	High (Can reach chemical accuracy) [3]	Potentially High (With sufficient data and resources)
Flexibility & Customization	Low (Constrained by original architecture)	Moderate (Limited architectural changes)	High (Full control over architecture and training) [70]
Risk of Overfitting	Not applicable	Medium (Especially with small datasets) [70]	Medium to High (Depending on data volume) [70]
Avoidance of Pre-trained Biases	Low	Medium	High [70]

Table 2: Performance comparison for materials science applications (Based on MACE models fine-tuned on H₂/Cu system) [3].

Model Type	Training Data	Energy RMSE	Force RMSE	Data Efficiency
Out-of-the-Box MACE-MP	N/A (Pre-trained)	Higher	Higher	N/A
MACE-MP-f4 (Fine-tuned)	20% of dataset (664 configurations)	Low (Similar to from-scratch with full data)	Low (Similar to from-scratch with full data)	High (Achieves target accuracy with 1/5 the data)
From-Scratch MACE	100% of dataset (3,376 configurations)	Low	Low	Baseline

Decision Framework and Application Guidance

When to Use Each Approach

• Out-of-the-Box FMs are optimal for rapid prototyping, initial screening, educational purposes, and applications where the model's general knowledge suffices and specialized accuracy is not critical [3] [2].
• Fine-Tuned FMs represent the most practical choice for most specialized research applications, particularly when dealing with limited data (hundreds to thousands of samples), constrained computational resources, or when seeking to leverage transfer learning for improved performance on specific tasks such as predicting reaction barriers or molecular properties [3] [71].
• From-Scratch Models remain necessary for highly novel applications where no relevant pre-trained models exist, when maximum control over architecture and data is required (e.g., for regulatory compliance in healthcare), or when pursuing fundamental algorithmic research [70] [71].

Implementation Considerations for Fine-Tuning

Successful fine-tuning requires careful consideration of several factors:

• Layer Selection: Determining which layers to freeze versus update significantly impacts performance and data efficiency. Research indicates that freezing approximately 80% of layers (e.g., early interaction layers) while updating later layers (readouts and product layers) often optimizes performance while minimizing overfitting risk [3].
• Data Requirements: While fine-tuning reduces data needs substantially, data quality and relevance remain crucial. The fine-tuning dataset should adequately represent the target domain.
• Platform Selection: Integrated platforms like MatterTune provide standardized workflows for fine-tuning various FMs (MACE, ORB, MatterSim), offering features like distributed training and broad task support [2].

Experimental Protocols

Protocol 1: Frozen Transfer Learning for Materials Foundation Models

This protocol details the fine-tuning procedure used to achieve high data efficiency with MACE foundation models, as demonstrated for the H₂/Cu system [3].

Research Reagent Solutions:

• Foundation Model: Pre-trained MACE-MP model ("small", "medium", or "large" variants).
• Target Dataset: Task-specific dataset (e.g., 4,230 structures for H₂/Cu surface reactions).
• Software Tools: MACE software suite with mace-freeze patch for layer freezing [3].
• Computational Resources: GPU clusters for efficient training.

Procedure:

• Data Preparation: Curate and preprocess the target dataset. Split into training (80%), validation (10%), and test sets (10%).
• Model Selection: Choose an appropriate pre-trained MACE-MP model. The "small" model often provides optimal balance between performance and efficiency [3].
• Freezing Configuration: Configure the model to freeze specific layers. The MACE-MP-f4 configuration (freezing all but the readout, product, and last interaction layers) has demonstrated optimal performance [3].
• Fine-tuning: Train the model on the target dataset using standard optimization algorithms (e.g., Adam). Monitor loss on the validation set.
• Validation: Evaluate the fine-tuned model on the test set. Calculate RMSE for energies and forces. Compare against from-scratch models for benchmarking.

Protocol 2: Fine-Tuning for Drug Discovery Applications

This protocol outlines a general workflow for fine-tuning foundation models in pharmaceutical research.

Research Reagent Solutions:

• Foundation Models: Drug discovery FMs (e.g., for target discovery, molecular optimization).
• Domain-Specific Data: Proprietary assay results, molecular structures, or clinical data.
• Platforms: Frameworks like MatterTune for standardized fine-tuning [2].

Procedure:

• Task Definition: Clearly define the downstream task (e.g., toxicity prediction, binding affinity estimation).
• Model Adaptation: Modify the output head of the pre-trained FM to match the task (e.g., change output dimension for classification vs. regression).
• Progressive Fine-tuning: Initially use lower learning rates to avoid catastrophic forgetting of general knowledge. Gradually adjust learning rates based on validation performance.
• Multi-task Validation: Validate model performance on both the specialized task and related general tasks to ensure retained generalizability.

Workflow Visualization

Diagram 1: Decision workflow for selecting the appropriate modeling approach, highlighting the role of data availability and pre-trained model relevance.

The Scientist's Toolkit: Essential Research Reagents

Table 3: Key resources for implementing foundation model strategies in materials and drug discovery research.

Resource Category	Specific Tools/Models	Function and Application
Materials Foundation Models	MACE-MP, CHGNet, MatterSim, ORB [3] [2]	Pre-trained models for atomistic simulations and property prediction across diverse materials systems.
Drug Discovery Foundation Models	Various specialized FMs (>200 available) [9] [72]	Target identification, molecular optimization, and preclinical research applications.
Fine-Tuning Platforms	MatterTune [2]	Integrated platform supporting multiple FMs with distributed training and customizable fine-tuning.
Layer Freezing Tools	mace-freeze patch [3]	Enables frozen transfer learning for improved data efficiency and reduced catastrophic forgetting.
Benchmark Datasets	H₂/Cu surface reactions [3], Ternary alloys [3]	Standardized datasets for validating model performance on challenging systems.

The strategic selection between out-of-the-box, fine-tuned, and from-scratch foundation models significantly impacts research outcomes in materials science and drug discovery. While out-of-the-box FMs offer immediate utility for general applications, and from-scratch training provides maximum customization for novel domains, fine-tuning emerges as the most balanced approach for most specialized research applications. The demonstrated data efficiency of frozen transfer learning—achieving chemical accuracy with only 10-20% of the data required for from-scratch training—makes fine-tuning particularly valuable for research domains where data generation is costly and time-consuming [3]. As integrated platforms like MatterTune continue to lower adoption barriers [2], and the ecosystem of domain-specific FMs expands [9], fine-tuning strategies will play an increasingly central role in accelerating scientific discovery across both materials and pharmaceutical research.

Conclusion

Fine-tuning has emerged as a universal and indispensable strategy for transforming robust but general-purpose materials foundation models into highly accurate, system-specific tools. The evidence consistently shows that fine-tuning can dramatically improve predictive accuracy—reducing force errors by 5-15x and energy errors by several orders of magnitude—while being remarkably data-efficient. Techniques like frozen transfer learning and parameter-efficient methods (e.g., ELoRA) make this process accessible even with limited computational or data resources. For biomedical and clinical research, the implications are profound. The ability to reliably simulate complex molecular interactions, polymorphic transitions, and ion diffusion dynamics with near-ab initio accuracy opens new frontiers in rational drug design, excipient development, and understanding biological interfaces at the atomistic level. Future progress will depend on the continued development of user-friendly fine-tuning platforms, the creation of specialized biomedical datasets, and the exploration of these techniques for simulating ever more complex biological phenomena, ultimately accelerating the translation of computational insights into clinical applications.

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