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

18 October 2024, Version 1
This content is a preprint and has not undergone peer review at the time of posting.

Abstract

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.

Keywords

Microkinetics

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