Automatic Process Exploration Driven by Diversity in Local Atomic Environments: Beyond Look-Up Table Kinetic Monte Carlo

We present an efficient automatic process explorer (APE) framework to overcome the reliance on human intuition to empirically establish relevant elementary processes of a given system, e.g. in prevalent kinetic Monte Carlo (kMC) simulations based on fixed process lists. Use of a fuzzy machine-learning classification algorithm minimizes redundancy in the transition-state searches by driving them toward hitherto unexplored local atomic environments. APE application to island diffusion at a Pd(100) surface immediately reveals a large number of up to now disregarded low-barrier collective processes that lead to a significant increase in the kMC-determined island diffusivity as compared to classic surface hopping and exchange diffusion mechanisms.