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A reactive neural network framework for water-loaded acidic zeolites

Author

Listed:
  • Andreas Erlebach

    (Charles University)

  • Martin Šípka

    (Charles University
    Charles University)

  • Indranil Saha

    (Charles University)

  • Petr Nachtigall

    (Charles University)

  • Christopher J. Heard

    (Charles University)

  • Lukáš Grajciar

    (Charles University)

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.

Suggested Citation

  • Andreas Erlebach & Martin Šípka & Indranil Saha & Petr Nachtigall & Christopher J. Heard & Lukáš Grajciar, 2024. "A reactive neural network framework for water-loaded acidic zeolites," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-48609-2
    DOI: 10.1038/s41467-024-48609-2
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    References listed on IDEAS

    as
    1. Christopher J. Heard & Lukas Grajciar & Cameron M. Rice & Suzi M. Pugh & Petr Nachtigall & Sharon E. Ashbrook & Russell E. Morris, 2019. "Fast room temperature lability of aluminosilicate zeolites," Nature Communications, Nature, vol. 10(1), pages 1-7, December.
    2. Simon Batzner & Albert Musaelian & Lixin Sun & Mario Geiger & Jonathan P. Mailoa & Mordechai Kornbluth & Nicola Molinari & Tess E. Smidt & Boris Kozinsky, 2022. "E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    3. Matteo Fasano & Thomas Humplik & Alessio Bevilacqua & Michael Tsapatsis & Eliodoro Chiavazzo & Evelyn N. Wang & Pietro Asinari, 2016. "Interplay between hydrophilicity and surface barriers on water transport in zeolite membranes," Nature Communications, Nature, vol. 7(1), pages 1-8, November.
    4. So Takamoto & Chikashi Shinagawa & Daisuke Motoki & Kosuke Nakago & Wenwen Li & Iori Kurata & Taku Watanabe & Yoshihiro Yayama & Hiroki Iriguchi & Yusuke Asano & Tasuku Onodera & Takafumi Ishii & Taka, 2022. "Towards universal neural network potential for material discovery applicable to arbitrary combination of 45 elements," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    5. Massimo Bocus & Ruben Goeminne & Aran Lamaire & Maarten Cools-Ceuppens & Toon Verstraelen & Veronique Van Speybroeck, 2023. "Nuclear quantum effects on zeolite proton hopping kinetics explored with machine learning potentials and path integral molecular dynamics," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    6. Jonathan Vandermause & Yu Xie & Jin Soo Lim & Cameron J. Owen & Boris Kozinsky, 2022. "Active learning of reactive Bayesian force fields applied to heterogeneous catalysis dynamics of H/Pt," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
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    1. Swagata Roy & Johannes P. Dürholt & Thomas S. Asche & Federico Zipoli & Rafael Gómez-Bombarelli, 2024. "Learning a reactive potential for silica-water through uncertainty attribution," Nature Communications, Nature, vol. 15(1), pages 1-10, December.

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