2022
DOI: 10.26434/chemrxiv-2022-mwf0b
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Atomistic Insights into the Oxidation of Flat and Stepped Platinum Surfaces Using Large-Scale Machine Learning Potential-based Grand-Canonical Monte Carlo

Abstract: Understanding catalyst surface structure changes under reactive conditions has become an important topic with the increasing interest in operando measurement and modelling. In this work, we develop a workflow to build machine learning potentials (MLPs) for simulating complicated chemical systems with large spatial and time scales, in which the committee model strategy equips the MLP with uncertainty estimation, enabling active learning protocol. The methods are applied to constructing PtOx MLP based on explore… Show more

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