Research Articles
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.
Accelerating Discovery: Machine Learning and High-Throughput Strategies for Thermodynamic Stability of New Materials
The discovery of new inorganic materials with targeted properties is a cornerstone of advancements in energy, electronics, and catalysis.
Machine Learning for Predicting Thermodynamic Stability: Accelerating Inorganic Compound Discovery
Accurate prediction of thermodynamic stability is a critical bottleneck in the discovery of new inorganic compounds and materials for biomedical and energy applications.
Experimental Design Performance Analysis: A 2025 Guide for Robust Research and Drug Development
This article provides a comprehensive framework for researchers, scientists, and drug development professionals to analyze the performance of experimental designs.
ML-Guided vs. Traditional Synthesis: A 2025 Paradigm Shift in Drug Discovery
This article provides a comprehensive comparison for researchers and drug development professionals on the evolving landscape of chemical synthesis.