Research Articles
Ensemble Machine Learning with Electron Configuration: A New Paradigm for Predicting Thermodynamic Stability in Materials and Drug Discovery
Predicting thermodynamic stability is a critical yet resource-intensive challenge in materials science and drug development.
Automated Procedures for Thermodynamic Stability and Chemical Potentials: A Guide for Materials and Drug Development
This article provides a comprehensive overview of automated computational and experimental procedures for determining material thermodynamic stability and synthesizable chemical potential ranges.
Beyond Graphene: Discovering New 2D Wide Bandgap Semiconductors for Next-Generation Technology
This article explores the rapidly advancing field of two-dimensional (2D) wide bandgap semiconductors, a class of materials poised to revolutionize power electronics, optoelectronics, and biomedical devices.
Universal Phase Stability Networks: A Complex Network Theory Framework for Drug Discovery and Materials Design
This article explores the transformative potential of universal phase stability networks, analyzed through complex network theory, for accelerating discovery in materials science and drug development.
Hierarchy in Materials Phase Stability Networks: A New Paradigm for Pharmaceutical Development
This article explores the emerging concept of hierarchy in materials phase stability networks and its critical implications for pharmaceutical scientists and drug development professionals.
Beyond Lead Halides: Exploring New Stable ABX-Type Inorganic Materials for Next-Generation Biomedical Applications
The ABX family of materials, notably hybrid organic-inorganic perovskites (HOIPs) and related structures, holds immense transformative potential for biomedical applications, including drug delivery, biosensing, and imaging.
Inverse Design of Stable Inorganic Materials: AI-Driven Discovery and Validation
This article explores the transformative paradigm of inverse design for discovering stable inorganic materials, a stark departure from traditional trial-and-error methods.
Computational Discovery of Thermodynamically Stable Compounds: Machine Learning and High-Throughput Strategies for Materials and Drug Development
This article explores the transformative role of computational methods, particularly machine learning (ML) and high-throughput (HTP) density functional theory (DFT), in predicting the thermodynamic stability of new compounds.
Energy Above the Convex Hull (E_hull): A Comprehensive Guide to Stability, Prediction, and AI-Driven Design of Inorganic Materials
This article provides a comprehensive overview of the energy above the convex hull (E_hull), a critical metric for assessing the thermodynamic stability of inorganic materials.
Phase Stability Networks: A New Paradigm for Materials Science and Drug Development
This article explores the phase stability network of inorganic materials as a transformative framework for understanding material reactivity and thermodynamic relationships.