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Nanoscale chemical reaction exploration with a quantum magnifying glass

Author

Listed:
  • Katja-Sophia Csizi

    (Department of Chemistry and Applied Biosciences)

  • Miguel Steiner

    (Department of Chemistry and Applied Biosciences
    NCCR Catalysis)

  • Markus Reiher

    (Department of Chemistry and Applied Biosciences
    NCCR Catalysis)

Abstract

Nanoscopic systems exhibit diverse molecular substructures by which they facilitate specific functions. Theoretical models of them, which aim at describing, understanding, and predicting these capabilities, are difficult to build. Viable quantum-classical hybrid models come with specific challenges regarding atomistic structure construction and quantum region selection. Moreover, if their dynamics are mapped onto a state-to-state mechanism such as a chemical reaction network, its exhaustive exploration will be impossible due to the combinatorial explosion of the reaction space. Here, we introduce a “quantum magnifying glass” that allows one to interactively manipulate nanoscale structures at the quantum level. The quantum magnifying glass seamlessly combines autonomous model parametrization, ultra-fast quantum mechanical calculations, and automated reaction exploration. It represents an approach to investigate complex reaction sequences in a physically consistent manner with unprecedented effortlessness in real time. We demonstrate these features for reactions in bio-macromolecules and metal-organic frameworks, diverse systems that highlight general applicability.

Suggested Citation

  • Katja-Sophia Csizi & Miguel Steiner & Markus Reiher, 2024. "Nanoscale chemical reaction exploration with a quantum magnifying glass," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-49594-2
    DOI: 10.1038/s41467-024-49594-2
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    References listed on IDEAS

    as
    1. Miguel Steiner & Markus Reiher, 2024. "A human-machine interface for automatic exploration of chemical reaction networks," Nature Communications, Nature, vol. 15(1), pages 1-18, December.
    2. Jörg Behler & Gábor Csányi, 2021. "Machine learning potentials for extended systems: a perspective," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 94(7), pages 1-11, July.
    3. 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.
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