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 …

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 …

By adopting a perspective informed by contemporary liquid-state theory, we consider how to
train an artificial neural network potential to describe inhomogeneous, disordered systems …

The combination of transfer learning (TL) a low-level potential energy surface (PES) to a
higher level of electronic structure theory together with ring-polymer instanton (RPI) theory is …

The calculation of the anharmonic modes of small-to medium-sized molecules for assigning
experimentally measured frequencies to the corresponding type of molecular motions is …

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

Uncertainty quantification (UQ) to detect samples with large expected errors (outliers) is
applied to reactive molecular potential energy surfaces (PESs). Three methods–Ensembles …

With the establishment of machine learning (ML) techniques in the scientific community, the
construction of ML potential energy surfaces (ML-PES) has become a standard process in …