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

Since its inception nearly a half century ago, CHARMM has been playing a central role in
computational biochemistry and biophysics. Commensurate with the developments in …

Combustion is a complex chemical system which involves thousands of chemical reactions
and generates hundreds of molecular species and radicals during the process. In this work …

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 investigation of cold interactions between ions and neutrals has recently emerged as a
new scientific frontier at the interface of physics and chemistry. Here, we report a study of …

An overview of computational methods to describe high-dimensional potential energy
surfaces suitable for atomistic simulations is given. Particular emphasis is put on accuracy …

This work introduces a novel methodology for the quantification of uncertainties associated
with potential energy surfaces (PESs) computed from first-principles quantum mechanical …

Reactive molecular dynamics (MD) simulation is a powerful tool to study the reaction
mechanism of complex chemical systems. Central to the method is the potential energy …

Constructing accurate, high-dimensional molecular potential energy surfaces (PESs) for
polyatomic molecules is challenging. Reproducing kernel Hilbert space (RKHS) …

The rise of machine learning has greatly influenced the field of computational chemistry and
atomistic molecular dynamics simulations in particular. One of its most exciting prospects is …