The field of data-driven chemistry is undergoing an evolution, driven by innovations in
machine learning models for predicting molecular properties and behavior. Recent strides in …

Machine learning interatomic potentials (MLIPs) are one of the main techniques in the
materials science toolbox, able to bridge ab initio accuracy with the computational efficiency …

Message-passing graph neural network interatomic potentials (GNN-IPs), particularly those
with equivariant representations such as NequIP, are attracting significant attention due to …

Abstract Machine-learned potentials (MLPs) have exhibited remarkable accuracy, yet the
lack of general-purpose MLPs for a broad spectrum of elements and their alloys limits their …

The accuracy of a machine learning model is limited by the quality and quantity of the data
available for its training and validation. This problem is particularly challenging in materials …

The quest for next-generation energy-storage technologies has pivoted towards all-solid-
state batteries, primarily owing to their potential for enhanced safety and energy density. At …

This review highlights recent advances in machine learning (ML)‐assisted design of energy
materials. Initially, ML algorithms were successfully applied to screen materials databases …

As the most important solvent, water has been at the center of interest since the advent of
computer simulations. While early molecular dynamics and Monte Carlo simulations had to …

In the next half-century, physical chemistry will likely undergo a profound transformation,
driven predominantly by the combination of recent advances in quantum chemistry and …

Summary Machine Learning Interatomic Potential (MLIP) overcomes the challenges of high
computational costs in density-functional theory and the relatively low accuracy in classical …