In recent years, the use of machine learning (ML) in computational chemistry has enabled
numerous advances previously out of reach due to the computational complexity of …

Machine learning (ML) techniques applied to chemical reactions have a long history. The
present contribution discusses applications ranging from small molecule reaction dynamics …

Recent years have seen vast progress in the development of machine learned force fields
(MLFFs) based on ab-initio reference calculations. Despite achieving low test errors, the …

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

“Δ-machine learning” refers to a machine learning approach to bring a property such as a
potential energy surface (PES) based on low-level (LL) density functional theory (DFT) …

Artificial Neural Networks (NN) are already heavily involved in methods and applications for
frequent tasks in the field of computational chemistry such as representation of potential …

The application of machine learning methods in quantum chemistry has enabled the study of
numerous chemical phenomena, which are computationally intractable with traditional ab …

In this feature, we review the current capabilities of local electron correlation methods up to
the coupled cluster model with single, double, and perturbative triple excitations [CCSD (T)] …

Machine learning has become more and more popular in computational chemistry, as well
as in the important field of spectroscopy. In this concise review, we walk the reader through a …

Machine-learned potential energy surfaces (PESs) for molecules with more than 10 atoms
are typically forced to use lower-level electronic structure methods such as density functional …