We review progress in neural network (NN)-based methods for the construction of
interatomic potentials from discrete samples (such as ab initio energies) for applications in …

Quantitative simulations of electronically nonadiabatic molecular processes require both
accurate dynamics algorithms and accurate electronic structure information. Direct …

Dynamics of bimolecular reactions in the gas phase are of foundational importance in
combustion, atmospheric chemistry, interstellar chemistry, and plasma chemistry. These …

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 …

We present a general framework for the development of data-driven many-body (MB)
potential energy functions (MB-QM PEFs) that represent the interactions between small …

In this work, we present a general purpose deep neural network package for representing
energies, forces, dipole moments, and polarizabilities of atomistic systems. This so-called …

We present a generalization of the many-body energy (MB-nrg) theoretical/computational
framework that enables the development of data-driven potential energy functions (PEFs) for …

The direct molecular simulation (DMS) method is used to obtain shear viscosity data for non-
reacting air and its components by simulating isothermal, plane Poiseuille subsonic flows …

Polarizabilities play significant roles in describing dispersive and inductive interactions of
the atom and molecular systems. However, an accurate prediction of molecular …

Double hybrid organic–inorganic perovskites (DHOIPs) with excellent optoelectronic
properties and low production costs are promising in photovoltaic applications. However …