Designing and screening novel electrocatalysts, understanding electrocatalytic mechanisms
at an atomic level, and uncovering scientific insights lie at the center of the development of …

The field of computational chemistry has seen a significant increase in the integration of
machine learning concepts and algorithms. In this Perspective, we surveyed 179 open …

Molecular dynamics (MD) simulation techniques are widely used for various natural science
applications. Increasingly, machine learning (ML) force field (FF) models begin to replace ab …

Abstract Machine learning potentials are an important tool for molecular simulation, but their
development is held back by a shortage of high quality datasets to train them on. We …

Massive scale, in terms of both data availability and computation, enables important
breakthroughs in key application areas of deep learning such as natural language …

Conspectus Designing new materials is vital for addressing pressing societal challenges in
health, energy, and sustainability. The combination of physicochemical laws and empirical …

Data-driven interatomic potentials have emerged as a powerful tool for approximating ab
initio potential energy surfaces. The most time-consuming step in creating these interatomic …

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 …

Knowledge of critical properties, such as critical temperature, pressure, density, as well as
acentric factor, is essential to calculate thermo-physical properties of chemical compounds …

Neural networks (NNs) often assign high confidence to their predictions, even for points far
out of distribution, making uncertainty quantification (UQ) a challenge. When they are …