Work

Automatic Process Exploration through Machine Learning Assisted Transition State Searches

Year: 2025

Type: article

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.

Source: Physical Review Letters

DOI: 10.1103/physrevlett.134.096201

Authors: King Chun Lai, Patricia Poths, Sebastian Matera, Christoph Scheurer, Karsten Reuter

Institution: Fritz Haber Institute of the Max Planck Society

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $3,750

Funder: Alexander von Humboldt-Stiftung