Research Articles
Mitigating Inductive Bias for Robust Machine Learning in Material Stability Prediction
This article explores the critical challenge of inductive bias in machine learning (ML) models for predicting material stability, a key task in accelerating drug development and materials discovery.
Machine Learning for Material Stability Prediction: From Composition to Clinical Application
This article explores the transformative role of composition-based machine learning (ML) models in predicting material stability, a critical property for pharmaceutical development and advanced materials science.
Improving Accuracy in Machine Learning Predictions of Thermodynamic Stability: A Guide for Biomedical Researchers
This article provides a comprehensive guide for researchers and drug development professionals on enhancing the accuracy of machine learning models for predicting thermodynamic stability—a critical property in drug design and...
Graph Neural Networks for Interatomic Interactions: Advancing Stability Prediction in Drug Discovery and Materials Science
This article explores the transformative role of Graph Neural Networks (GNNs) in predicting interatomic interactions and system stability, a critical challenge in computational chemistry and drug development.
ECCNN: The Electron Configuration Convolutional Neural Network for Predictive Materials Science and Drug Discovery
This article explores the Electron Configuration Convolutional Neural Network (ECCNN), a novel machine learning framework that uses raw electron configuration data to predict material properties with exceptional accuracy and sample...
Stacked Generalization Machine Learning: Advanced Predictive Modeling for Material Stability and Drug Development
This article provides a comprehensive exploration of stacked generalization, an advanced ensemble machine learning technique, and its application in predicting material stability and properties crucial for drug development.
CPLAP: A Comprehensive Guide to Chemical Potential Analysis for Materials and Drug Discovery
This article provides a complete overview of the Chemical Potential Limits Analysis Program (CPLAP), a computational tool critical for determining material stability and thermodynamic properties.
First-Principles Calculations: Validating Stable Compounds for Advanced Materials and Drug Development
This article provides a comprehensive overview of how first-principles calculations, rooted in density functional theory (DFT), are revolutionizing the validation of stable compounds in materials science and drug development.
Machine Learning for Predicting Energy Above Convex Hull in Inorganic Materials: A Comprehensive Guide
This article provides a comprehensive overview of how machine learning (ML) is revolutionizing the prediction of the energy above the convex hull, a key metric for assessing the thermodynamic stability...
High-Throughput DFT for Thermodynamic Stability Screening: Accelerating Materials and Drug Discovery
This article provides a comprehensive overview of high-throughput Density Functional Theory (DFT) calculations for thermodynamic stability screening, a transformative approach in materials science and drug development.