Learning reduced kinetic Monte Carlo models of complex chemistry from molecular dynamics

Abstract

We propose a novel statistical learning framework for automatically and efficiently building reduced kinetic Monte Carlo (KMC) models of large-scale elementary reaction networks from data generated by a single or few molecular dynamics simulations (MD).