Research Articles
Latent Space Bayesian Optimization: Accelerating Advanced Materials and Drug Discovery
This article explores the transformative potential of Bayesian Optimization (BO) within latent spaces for accelerating the discovery and design of novel materials and molecules.
Improving Generative AI Property Prediction for Accelerated Materials and Drug Discovery
This article provides a comprehensive analysis of strategies to enhance the property prediction accuracy of generative artificial intelligence models in molecular and materials design.
Steady-State vs. Pre-Steady-State Kinetics: A Comprehensive Guide for Drug Discovery and Enzyme Analysis
This article provides a comprehensive comparison of steady-state and pre-steady-state kinetic analysis, tailored for researchers, scientists, and drug development professionals.
From Prediction to Reality: Experimental Validation of High-Entropy Oxide Synthesizability
The discovery of new high-entropy oxides (HEOs) is transitioning from serendipitous experimental finding to rational design, driven by advanced computational predictions.
Overcoming Kinetic Hurdles in Cope Rearrangement: Catalytic Strategies and Modern Applications for Synthesis
This article provides a comprehensive analysis of the kinetic challenges inherent to the Cope rearrangement and the advanced strategies developed to overcome them.
Optimizing Mass Transfer Kinetics in Membrane Formation: Strategies for Enhanced Performance in Biomedical Applications
This article provides a comprehensive analysis of strategies to improve mass transfer kinetics during membrane formation, a critical factor determining the structural and functional properties of membranes used in drug...
Kinetic vs Thermodynamic Control: Mastering Reaction Pathways for Drug Discovery and Development
This article provides a comprehensive analysis of kinetic and thermodynamic reaction control, tailored for researchers and professionals in drug development.
Machine Learning for Solid-State Synthesis: Predicting Routes and Accelerating Materials Discovery
This article explores the transformative role of machine learning (ML) in predicting and planning solid-state synthesis routes, a critical bottleneck in materials discovery.
Validating Predicted Topological Semimetals: From Computational Discovery to Experimental Confirmation
This article provides a comprehensive framework for the validation of predicted topological semimetals, addressing the critical gap between computational prediction and experimental confirmation.
Data Cleaning for Materials Informatics: Advanced Techniques to Overcome Sparse, Noisy Data and Accelerate Discovery
This article provides a comprehensive guide to data cleaning techniques specifically tailored for the unique challenges in materials informatics.