A reactive neural network framework for water-loaded acidic zeolites

Andreas Erlebach (), Martin Šípka, Indranil Saha, Petr Nachtigall, Christopher J. Heard and Lukáš Grajciar ()
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Andreas Erlebach: Charles University
Martin Šípka: Charles University
Indranil Saha: Charles University
Petr Nachtigall: Charles University
Christopher J. Heard: Charles University
Lukáš Grajciar: Charles University

Nature Communications, 2024, vol. 15, issue 1, 1-14

Abstract: Abstract Under operating conditions, the dynamics of water and ions confined within protonic aluminosilicate zeolite micropores are responsible for many of their properties, including hydrothermal stability, acidity and catalytic activity. However, due to high computational cost, operando studies of acidic zeolites are currently rare and limited to specific cases and simplified models. In this work, we have developed a reactive neural network potential (NNP) attempting to cover the entire class of acidic zeolites, including the full range of experimentally relevant water concentrations and Si/Al ratios. This NNP has the potential to dramatically improve sampling, retaining the (meta)GGA DFT level accuracy, with the capacity for discovery of new chemistry, such as collective defect formation mechanisms at the zeolite surface. Furthermore, we exemplify how the NNP can be used as a basis for further extensions/improvements which include data-efficient adoption of higher-level (hybrid) references via Δ-learning and the acceleration of rare event sampling via automatic construction of collective variables. These developments represent a significant step towards accurate simulations of realistic catalysts under operando conditions.

Date: 2024
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DOI: 10.1038/s41467-024-48609-2

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