Research Articles
Mastering Seeding Protocols for Secondary Nucleation Control: From Foundational Principles to Advanced Applications in Drug Development
This comprehensive article provides researchers, scientists, and drug development professionals with an in-depth examination of seeding protocols for secondary nucleation control.
Evaluating SMILES vs. SELFIES: A Comprehensive Guide to Accuracy in Molecular Representation for Drug Discovery
This article provides a critical evaluation of the accuracy of SMILES and SELFIES molecular representations for AI-driven drug discovery.
Combating Overfitting: Practical Strategies for Robust Machine Learning on Small Materials Science Datasets
This article provides a comprehensive guide for researchers and professionals in materials science and drug development on mitigating overfitting in machine learning models when working with limited datasets.
Inverse Design of Materials Using Deep Generative Models: A Comprehensive Guide for Researchers
This article provides a comprehensive overview of the rapidly evolving field of inverse materials design using deep generative models.
Beyond Charge-Balancing: How Deep Learning is Redefining Synthesizability Prediction in Drug Discovery and Materials Science
Predicting whether a proposed molecule or material can be successfully synthesized is a critical challenge in accelerating discovery.
Optimizing Deep Learning Parameters for Accurate Material Synthesizability Prediction in Drug Development
Accelerating the discovery of synthesizable materials is a critical bottleneck in drug development.
Overcoming Data Scarcity in Machine Learning for Inorganic Synthesis: Strategies for Biomedical Innovation
This article addresses the critical challenge of data scarcity that impedes the application of machine learning (ML) in inorganic materials synthesis, a key bottleneck in accelerating the discovery of new...
Machine Learning for Solid-State Synthesis: Predicting Routes and Accelerating Materials Discovery
This article explores the transformative role of machine learning (ML) in predicting and planning solid-state synthesis routes, a critical bottleneck in materials discovery.
Beyond Thermodynamics: Tackling the Fundamental Challenges in Predicting Synthesizable Inorganic Crystals
The acceleration of computational materials design has starkly contrasted with the slow, empirical nature of experimental synthesis, creating a critical bottleneck in materials discovery.
Navigating the Energy Landscape: AI-Driven Strategies for Advanced Inorganic Materials Synthesis
The synthesis of novel inorganic materials is fundamentally governed by navigating complex, multi-dimensional energy landscapes, where the discovery of both stable ground states and valuable metastable phases resides.