Research Articles
Predicting Synthesis Feasibility of Inorganic Materials: AI, Machine Learning, and Data-Driven Approaches
The acceleration of inorganic materials discovery is critically dependent on accurately predicting synthesis feasibility.
Bridging Theory and Practice: Mastering Solid-State Reaction Thermodynamics and Kinetics in Pharmaceutical Development
This article provides a comprehensive analysis of the interplay between thermodynamics and kinetics in solid-state reactions, with a specific focus on applications in pharmaceutical research and drug development.
Solid-State Reactions in Inorganic Materials: From Foundational Principles to Advanced Applications in Biomedicine
This article provides a comprehensive examination of the basic principles governing solid-state reactions for inorganic materials, a cornerstone of modern inorganic chemistry.
Thermodynamic Stability of Binary, Ternary, and Quaternary Materials: A Comparative Guide for Materials and Pharmaceutical Research
This article provides a comprehensive comparison of thermodynamic stability across binary, ternary, and quaternary material systems, tailored for researchers and drug development professionals.
Quantifying Reactivity: The Nobility Index as a Predictive Metric in Materials Science and Drug Development
This article explores the 'Nobility Index,' a data-driven metric for quantifying material reactivity, and its transformative potential for researchers and drug development professionals.
Benchmarking Roost, Magpie, and ECCNN: A Comparative Analysis of Machine Learning Models for Thermodynamic Stability Prediction in Materials Science and Drug Development
This article provides a comprehensive benchmark analysis of three prominent machine learning models—Roost, Magpie, and ECCNN—for predicting thermodynamic stability of inorganic compounds, with specific relevance to biomedical and materials research.
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.