Abstract Machine learning (ML) is becoming a method of choice for modelling complex
chemical processes and materials. ML provides a surrogate model trained on a reference …

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 …

Atomistic simulation has a broad range of applications from drug design to materials
discovery. Machine learning interatomic potentials (MLIPs) have become an efficient …

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 …

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

All-inorganic perovskites are promising candidates for solar energy and optoelectronic
applications, despite their polycrystalline nature with a large density of grain boundaries …

Inorganic 2D materials offer a new approach to controlling mass diffusion at the nanoscale.
Controlling ion transport in nanofluidics is key to energy conversion, energy storage, water …

The electrochemical interface, where the adsorption of reactants and electrocatalytic
reactions take place, has long been a focus of attention. Some of the important processes on …

Computational programs accelerate the chemical discovery processes but often need
proper three-dimensional molecular information as part of the input. Getting optimal …