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

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 …

This work presents a detailed investigation of the energy-transfer and dissociation
mechanisms in N2 (X1Σg+)+ O (3P) and NO (X2Π)+ N (4S) systems using rovibrational …

The state-to-state (STS) inelastic energy transfer and O-atom exchange reaction between O
and CO (v), as two fundamental processes in non-equilibrium air flow around spacecraft …

A machine-learned model for predicting product state distributions from specific initial states
(state-to-distribution or STD) for reactive atom–diatom collisions is presented 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 …

The atom-exchange and atomization dissociation dynamics for the N (4S)+ N2 (1Σg+)
reaction are studied using a reproducing kernel Hilbert space (RKHS)-based, global …

Potential energy surface calculations yield physical insight into the structure of intermediates
and the dynamics of molecular collisions, and they are the first step toward molecular …

The prediction of product translational, vibrational, and rotational energy distributions for
arbitrary initial conditions for reactive atom+ diatom collisions is of considerable practical …

All-atom simulations can provide molecular-level insights into the dynamics of gas-phase,
condensed-phase and surface processes. One important requirement is a sufficiently …