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

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 …

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 …

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

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 …

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 …

The development of reliable and extensible molecular mechanics (MM) force fields—fast,
empirical models characterizing the potential energy surface of molecular systems—is …