Machine learning (ML) techniques applied to chemical reactions have a long history. The
present contribution discusses applications ranging from small molecule reaction dynamics …

With advances in ab initio theory, it is now possible to calculate electronic energies within
chemical (< 1 kcal/mol) accuracy. However, it is still challenging to represent faithfully a …

In this Perspective, we review recent advances in constructing high-fidelity potential energy
surfaces (PESs) from discrete ab initio points, using machine learning tools. Such PESs …

Hypersonic flows are energetic and result in regions of high temperature, causing internal
energy excitation, chemical reactions, ionization, and gas-surface interactions. At typical …

A rigorous, general, and simple method to fit global and permutation invariant potential
energy surfaces (PESs) using neural networks (NNs) is discussed. This so-called …

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

Accurate modeling of high-temperature hypersonic flows in the atmosphere requires
consideration of collision-induced dissociation of molecular species and energy transfer …

In the early afternoon of Tuesday, 3 October 1967, a ramjet engine fell out of the southern
California sky. Moments earlier, it had been attached to the underbelly of an experimental …

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 …

Hydrocarbons are ubiquitous as fuels, solvents, lubricants, and as the principal components
of plastics and fibers, yet our ability to predict their dynamical properties is limited to force …