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.

A Practical Toolkit: Fine-Tuning Methods and Platforms

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 [1].

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 [1] [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 Hâ‚‚/Cu System [1]

Model Type	Training Data Used	Energy RMSE (meV/atom)	Force RMSE (meV/Ã…)	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
ML267	ML267, MF:C19H18ClF6N5O3S, MW:545.9 g/mol	Chemical Reagent	Bench Chemicals
AZ4800	AZ4800, MF:C24H29N5O3, MW:435.5 g/mol	Chemical Reagent	Bench Chemicals

Table 2: Impact of Foundation Model Size on Fine-Tuning Efficiency [1]

Foundation Model	Number of Parameters	Relative Fine-Tuning Compute	Final Accuracy on Hâ‚‚/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 [1]. 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 [3]. The "small" foundation model is often sufficient for fine-tuning, offering an optimal balance between performance and computational cost [1].

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 Hâ‚‚ on Cu surfaces [1].

• Objective: To create a highly accurate and data-efficient machine learning interatomic potential for Hâ‚‚/Cu reactive dynamics.
• Primary Materials & Models:
  • Foundation Model: Pre-trained MACE-MP "small" model [1] [2].
  • Target Data: A dataset of ~4,230 structures with energies and forces for Hâ‚‚/Cu systems, derived from first-principles calculations and active learning [1].
  • Software: MACE software suite with the mace-freeze patch [1].

Step-by-Step Procedure:

• Data Preparation and Partitioning:

  • Curate your target dataset (e.g., Hâ‚‚/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 [1].
  • 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 [1].

• 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 [1] [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 [1] [4].
  • 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 [1].

Workflow and Strategy Visualization

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

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 [1] [2]
mace-freeze Patch	Software Tool	Enables layer-freezing for fine-tuning within the MACE software suite.	[1]
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 [1] [2]
Hâ‚‚/Cu Surface Dataset	Target Dataset	Task-specific dataset for benchmarking fine-tuning performance on reactive chemistry.	4,230 structures [1]
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.	[1]

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 [5]. 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 [6] [7].

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 [1] [8]. 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 [5] [9]. 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 [9].

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 [5].
• Modularity: Multiple, task-specific LoRA adapters can be trained independently and swapped on top of a single, frozen base model [10].

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 [6] [11].

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 [6] [7].

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 [11]. This method prevents the mixing of features from different tensor orders, thereby strictly preserving the equivariance property throughout the fine-tuning process [6]. 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 [7].

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 [6] [7]	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 [11]	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) [1]	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 [11].

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 [8] [2].
Target Dataset	The small, task-specific dataset for adaptation.	A few hundred to a few thousand local structures of the target molecular system [11].
ELoRA Adapter Modules	The trainable, path-specific low-rank matrices injected into the base model.	Rank r is a key hyperparameter; code available at [6].
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 [9].

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 [9].
  • 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 [1].

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) [8] [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) [1].
• 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 [1].

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 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 [12].

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 [1] [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 [13] [14].

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) [13].

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 [1]	Many-body equivariant messages	Materials Project (MPtrj)	High accuracy on inorganic and molecular systems
CHGNet [1]	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] [12]	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 [13].

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) [1]	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) [13]	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 [13].
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 [1].
Multi-Head Fine-Tuning [1]	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 [13].

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 [12].

Table 3: Benchmarking Fine-Tuning Performance Across MLIP Architectures [12]

Model Architecture	Foundation Model Force RMSE (meV/Ã…)	Fine-Tuned Model Force RMSE (meV/Ã…)	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) [12]. 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 [13].

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 [1].

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 [1]. 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 [1].

• 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 [1].
  • 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 [13].

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 [12].	Information included with reference [12]
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 [15].	Methodology described in reference [15]
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 [13]
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/
S1PL-IN-1	S1PL-IN-1, MF:C26H23ClN6, MW:455.0 g/mol	Chemical Reagent	Bench Chemicals
FATP1-IN-1	FATP1-IN-1, MF:C18H22FN5OS, MW:375.5 g/mol	Chemical Reagent	Bench Chemicals

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 [13]. 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 [12] 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 [1]. 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 [16] [17].

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 [1]	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) [1]	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). [1]	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 [1].

1. Prerequisite Model and Software Setup

• Foundation Model: Obtain a pre-trained MACE-MP model ("small," "medium," or "large") [1] [18].
• Software: Install the MACE software suite and the mace-freeze patch, which enables layer freezing [1].
• Environment: Ensure access to a Python environment with libraries such as ASE for atomistic simulations [18].

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 Hâ‚‚ dissociation on Cu surfaces contains 4,230 structures [1].
• 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 [1].
• 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 [1].

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 [18]
mace-freeze Patch	Software Tool	Enables layer freezing during fine-tuning of MACE models.	MACE software suite patch [1]
ASE (Atomic Simulation Environment)	Python Library	Facilitates setting up, running, and analyzing atomistic simulations.	https://wiki.fysik.dtu.dk/ase/ [18]
RBMD Package	Simulation Platform	Enables large-scale particle simulations integrated with MLIPs.	Random Batch Molecular Dynamics [18]
PEFT Libraries	Code Library	Provides implementations of Parameter-Efficient Fine-Tuning methods like LoRA.	Hugging Face PEFT Library [16]
Go 7874	Go 7874, MF:C27H26N4O4, MW:470.5 g/mol	Chemical Reagent	Bench Chemicals
eCF506	eCF506, MF:C26H38N8O3, MW:510.6 g/mol	Chemical Reagent	Bench Chemicals

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 [1]. 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 [19] [20]. 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 [12] [21]. While these models demonstrate impressive transferability, their out-of-the-box accuracy often remains insufficient for predicting subtle energetic differences in specialized applications [1] [13].

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 [12]. 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 [1] [21].

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
Hâ‚‚/Cu Surface Reactions	MACE-MP	664 configurations (20% of full set)	Similar accuracy to from-scratch model trained on 3,376 configurations	[1]
Ice Polymorph Sublimation Enthalpies	MACE-MP-0	~50 training structures	Sub-kJ/mol accuracy in sublimation enthalpies; <1% error in densities	[22] [21]
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	[12]
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	[13]

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 [21]. 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) [1].

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 [12]. 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 [1]. 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 [1]. 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 [21]. 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 [21]. 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 [1].

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 [21].

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 [1].

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 [12]. 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 [13]. 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 [12] [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 [1]. 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 [1] [10]. 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 [10]. 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 [1] [23]. 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 [1].

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 [1]

Model Name	Frozen Layers	Trainable Parameters	Data Efficiency	Force RMSE (eV/Ã…)	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) [1]. 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 [1] [23].

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) [12].
• Foundation Model Selection: Choose a suitable pre-trained model. Common choices in materials science include:
  • MACE-MP: Known for high accuracy and equivariant features [1] [23].
  • MatterSim: A universal potential trained on a vast dataset [2] [12].
  • ORB: A non-conservative, invariant model that predicts forces directly [12].
• 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 [12].
  • Content: The dataset must contain atomic structures, total energies, and atomic forces [23].
  • 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 [1]:
    • 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 [1].
  • 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) [23].

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 [1] [12].
  • 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 [12] [23].
• 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. [23]	[1] [23]
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. [12]	[12]
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] [23]	[2] [23]
Materials Project Database	Pre-training Data	A large repository of DFT calculations used to train many foundation models, providing broad coverage of inorganic materials. [12]	[12]
Target-Specific Dataset	Fine-Tuning Data	A smaller, high-fidelity dataset generated from first-principles calculations, tailored to the specific scientific problem.	[1] [23]
CAA-0225	CAA-0225, MF:C28H29N3O5, MW:487.5 g/mol	Chemical Reagent	Bench Chemicals

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 [1]. 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) [12]. 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 [1] [12]. 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 [12]. 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) [24]. 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 [24].

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 [24]. 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 [24]. 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 [24].

• 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 [24].

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 [24].

• 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 [24].

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 [24].

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 [25]. 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 [1].

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 [1].

• 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 [1].

• 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 [1].

• 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 [1].

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 [24].

• Identify Bottlenecks: Use monitoring tools to identify specific bottlenecks - common issues include slow data loading (CPU-bound), inefficient memory access, or poor parallelization [24].

• 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 [24]

• 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 [1]
aMACEing Toolkit	Unified Interface	Command-line interface for fine-tuning workflows across multiple MLIP frameworks	Standardized fine-tuning across MACE, GRACE, SevenNet, MatterSim, ORB [12]
Neptune	Experiment Tracker	Monitoring and evaluation tool for foundation model training experiments	Tracking fine-tuning experiments across multiple GPU nodes [26]
Service Fabric	Cluster Manager	Standalone orchestration for on-premises research clusters	Managing specialized node types for interactive vs. batch processing [25]

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 [27] [12] [28]. 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 [1] [12]. 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 [1] [2] [12]. 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 [12].

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

Model Architecture	System	Force RMSE (meV/Ã…)	Energy RMSE (meV/atom)
MACE (Foundation)	CsHâ‚‚POâ‚„	125 - 180	8.5 - 12.0
MACE (Fine-Tuned)	CsHâ‚‚POâ‚„	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 [12].

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 [12].
  • 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 [1] [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 [1]. For MACE models, freezing up to the first 4 interaction layers has shown optimal performance [1].
• 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 [29].
  • 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/Ã… and an energy RMSE of < 2 meV/atom on the test set [12]. Errors for fine-tuned models on the Hâ‚‚/Cu system reached ~25 meV/Ã… for forces using only hundreds of data points [1].

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 [12].

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] [12]
Fine-Tuning Platforms	Software Framework	Simplifies the fine-tuning process with unified interfaces and pre-built workflows.	MatterTune, aMACEing Toolkit [2] [12]
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 [12] [29]

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 [12]. 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 [12]. 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/Ã…)	Fine-tuned Force MAE (meV/Ã…)	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 [12].

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/Ã…)	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 [1].

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 [12]

• 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 [12] [30]
  • 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 [12]

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 [12]

• Fine-tuning Strategy:

  • Frozen Transfer Learning: Freeze initial layers (e.g., 4-6 layers in MACE) and update only readout layers [1]
  • 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 [12]

• 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 [1]

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 [12]
MACE-freeze	Software Patch	Frozen transfer learning implementation	Enables layer freezing for data-efficient fine-tuning [1]
Materials Project	Database	DFT calculations of 200,000+ materials	Source of pre-training data for foundation models [30]
Open Materials 2024	Database	100M+ DFT calculations	Large-scale diverse training data [12]
NVIDIA DGX Systems	Hardware	GPU computing infrastructure	High-performance training and fine-tuning [10]

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 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 [27]. 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 [31]. 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 [32].

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 [33]. 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 [34].

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 [35].

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 [27]. 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 [36].

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 [35].

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	[35]
MLP Model	CO₂ in brine	P: up to 100 MPa, T: up to 673°K	RMSE: 2.945, R²: 0.998	[36]
Active Learning with MCM	Binary mixtures at infinite dilution	298 K	Almost 50% reduction in relative mean squared error	[33]
Molecular Dynamics Simulations	Lennard-Jones binary mixtures	Various state points	Reference data for model validation	[35]

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/Iâ‚€) = -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 [33].

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 [34].

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 [34].

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 [34].

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 [37].

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 [37].

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 [37].

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 [38]
Molecular Graphs	Graph-based structural representation	Captures atomic interactions and topology [34]
Chemical Vectors	118-dimensional element representation	Phase diagram prediction in FerroAI [37]
Lennard-Jones Potential Parameters	Molecular interaction modeling	Reference data for diffusion in mixtures [35]
PFG-NMR Spectroscopy	Diffusion coefficient measurement	Experimental validation of predicted diffusion [33]
Temperature-Controlled XRD	Crystal structure determination	Phase transition validation [37]
Microscale Thermophoresis	Binding affinity measurement	Drug-target interaction validation [34]

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.

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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