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 …

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 …

Machine learning (ML) has emerged as a formidable force for identifying hidden but
pertinent patterns within a given data set with the objective of subsequent generation of …

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

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 …

Direct molecular dynamics (MD) simulation with ab initio quantum mechanical and
molecular mechanical (QM/MM) methods is very powerful for studying the mechanism of …

Abstract Machine learning techniques have been widely applied in many fields of chemistry,
physics, biology, and materials science. One of the most fruitful applications is machine …

Despite the ever-increasing computer power, accurate ab initio calculations for large
systems (thousands to millions of atoms) remain infeasible. Instead, approximate empirical …

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