The electrical double layer (EDL) plays a central role in electrochemical energy systems,
impacting charge transfer mechanisms and reaction rates. The fundamental importance of …

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 …

Engineering stabilized proteins is a fundamental challenge in the development of industrial
and pharmaceutical biotechnologies. We present Stability Oracle: a structure-based graph …

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 …

Abstract Machine learning (ML) models, if trained to data sets of high-fidelity quantum
simulations, produce accurate and efficient interatomic potentials. Active learning (AL) is a …

Fast and accurate calculation of intermolecular interaction energies is desirable for
understanding many chemical and biological processes, including the binding of small …

Machine Learning (ML) is increasingly applied to fill data gaps in assessments to quantify
impacts associated with chemical emissions and chemicals in products. However, the …

Recent advances in equivariant graph neural networks (GNNs) have made deep learning
amenable to develo** fast surrogate models to expensive ab initio quantum mechanics …

Lithium‐ion batteries (LIBs) have played an essential role in the energy storage industry and
dominated the power sources for consumer electronics and electric vehicles. Understanding …

We present a transferable MACE interatomic potential that is applicable to open-and closed-
shell drug-like molecules containing hydrogen, carbon, and oxygen atoms. Including an …