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 …

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 …

In recent years, machine learning potentials (MLP) for atomistic simulations have attracted a
lot of attention in chemistry and materials science. Many new approaches have been …

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 …

Advances in the development of quantum chemical methods and progress in multicore
architectures in computer science made the simulation of infrared spectra of isolated …

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 …

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 …

Full-dimensional potential energy surfaces (PESs) based on machine learning (ML)
techniques provide a means for accurate and efficient molecular simulations in the gas and …

Atomistic simulations using accurate energy functions can provide molecular-level insight
into functional motions of molecules in the gas and in the condensed phase. This …

We construct a full-dimensional ab initio neural network potential energy surface (PES) for
the isomerization system of the formic acid dimer (FAD). This is based upon ab initio …