Research Articles
Thermodynamic Stability of Perovskite Oxides: A Comparative Guide from Fundamentals to AI-Driven Discovery
This article provides a comprehensive comparison of the thermodynamic stability of perovskite oxides, a critical property governing their synthesizability and application longevity.
From Simulation to Synthesis: A Roadmap for the Experimental Realization of Theoretically Predicted Materials
This article provides a comprehensive guide for researchers and drug development professionals on translating computationally predicted stable materials into experimentally validated realities.
Composition vs. Structure: A Comparative Guide to Stability Models in Drug Development
This article provides a comprehensive comparison of composition-based and structure-based models for predicting molecular stability, a critical factor in drug discovery and development.
Ensemble Machine Learning for Thermodynamic Stability Prediction: A Comparative Guide for Materials and Pharmaceutical Research
Predicting thermodynamic stability is a fundamental challenge in materials science and pharmaceutical development.
Handling Data Skew for Robust Feature Distribution and Stability Prediction in Drug Development
This article provides a comprehensive guide for researchers and drug development professionals on managing data skew to ensure reliable feature distribution and stability predictions.
Reducing Computational Cost in DFT Stability Calculations: 2025 Guide with Machine Learning & Best Practices
Density Functional Theory (DFT) is a cornerstone of computational chemistry and materials science, but its high computational cost remains a major bottleneck for high-throughput screening and large-scale dynamic simulations, particularly...
Feature Selection Engineering for Thermodynamic Stability Models: A Guide for Drug Development
This article provides a comprehensive guide for researchers and drug development professionals on applying feature selection engineering to build robust and predictive models for thermodynamic stability.
Correcting Unphysical Predictions in Defect Formation Energies: From Computational Fixes to AI Solutions
Accurate prediction of defect formation energies is paramount for advancing materials science, influencing properties from chemical reactivity to conductivity.
Improving Stability in Small-Sample Prediction Models: Methods for Biomedical and Clinical Research
This article addresses the critical challenge of model instability in stability prediction when working with limited sample sizes, a common scenario in biomedical research and drug development.
Optimizing Chemical Potential Ranges for Advanced Material Formation: Strategies for Researchers and Drug Developers
This article provides a comprehensive guide for researchers and drug development professionals on optimizing chemical potential ranges to control material formation.