IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v15y2024i1d10.1038_s41467-024-49594-2.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-024-49594-2
    File Function: Abstract
    Download Restriction: no

    File URL: https://libkey.io/10.1038/s41467-024-49594-2?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    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. 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.
    3. Kangming Li & Daniel Persaud & Kamal Choudhary & Brian DeCost & Michael Greenwood & Jason Hattrick-Simpers, 2023. "Exploiting redundancy in large materials datasets for efficient machine learning with less data," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    4. Yuta Sakanaka & Shotaro Hiraide & Iori Sugawara & Hajime Uematsu & Shogo Kawaguchi & Minoru T. Miyahara & Satoshi Watanabe, 2023. "Generalised analytical method unravels framework-dependent kinetics of adsorption-induced structural transition in flexible metal–organic frameworks," Nature Communications, Nature, vol. 14(1), pages 1-12, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-49594-2. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.