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 …

Quantum-chemistry simulations based on potential energy surfaces of molecules provide
invaluable insight into the physicochemical processes at the atomistic level and yield such …

Computational study of molecules and materials from first principles is a cornerstone of
physics, chemistry, and materials science, but limited by the cost of accurate and precise …

Atomistic machine learning (AML) simulations are used in chemistry at an everincreasing
pace. A large number of AML models has been developed, but their implementations are …

Conspectus Photochemical reactions are of great importance in chemistry, biology, and
materials science because they take advantage of a renewable energy source, mild reaction …

We wish to describe a potential energy surface by using a basis of permutationally invariant
polynomials whose coefficients will be determined by numerical regression so as to …

The KREG and pKREG models were proven to enable accurate learning of
multidimensional single-molecule surfaces of quantum chemical properties such as ground …

The structure and dynamics of a molecular system is governed by its potential energy
surface (PES), representing the total energy as a function of the nuclear coordinates …

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 …