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

Conspectus Photochemical reactions are of great importance in chemistry, biology, and
materials science because they take advantage of a renewable energy source, mild reaction …

Revealing the origins of kinetic selectivity is one of the premier tasks of applied theoretical
organic chemistry, and for many reactions, doing so involves comparing competing …

Recent advances in the development of reactive machine-learned potentials (MLPs)
promise to transform reaction modelling. However, such methods have remained …

The all-atomic full-dimensional-level simulations of nonadiabatic molecular dynamics
(NAMD) in large realistic systems has received high research interest in recent years …

The machine learning of potential energy surfaces (PESs) has undergone rapid progress in
recent years. The vast majority of this work, however, has been focused on the learning of …

Storing solar energy is a vital component of using renewable energy sources to meet the
growing demands of the global energy economy. Molecular solar thermal (MOST) energy …

Machine learning (ML) continues to revolutionize computational chemistry for accelerating
predictions and simulations by training on experimental or accurate but expensive quantum …

Direct excitation of aromatic compounds grants access to high‐energy intermediates that
can be utilised in organic synthesis. Understanding and predicting the substituent effects at …

Abstract Machine learning photodynamics simulations are revolutionary tools to resolve
elusive photochemical reaction mechanisms with time-dependent high-fidelity structure …