In the past two decades, machine learning potentials (MLPs) have driven significant
developments in chemical, biological, and material sciences. The construction and training …

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 …

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 …

The atomic cluster expansion (ACE) was proposed recently as a new class of data-driven
interatomic potentials with a formally complete basis set. Since the development of any …

Abstract Machine learning interatomic potentials (MLIPs) enable accurate simulations of
materials at scales beyond that accessible by ab initio methods and play an increasingly …

Solvent effects influence all stages of the chemical processes, modulating the stability of
intermediates and transition states, as well as altering reaction rates and product ratios …

Quantum chemical simulations can be greatly accelerated by constructing machine learning
potentials, which is often done using active learning (AL). The usefulness of the constructed …

Identifying optimal collective variables to model transformations using atomic-scale
simulations is a long-standing challenge. We propose a new method for the generation …

The emergence of artificial intelligence is profoundly impacting computational chemistry,
particularly through machine-learning interatomic potentials (MLIPs). Unlike traditional …

Simulating catalytic reactivity under operative conditions poses a significant challenge due
to the dynamic nature of the catalysts and the high computational cost of electronic structure …