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Infusing theory into deep learning for interpretable reactivity prediction

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  • Shih-Han Wang

    (Virginia Polytechnic Institute and State University)

  • Hemanth Somarajan Pillai

    (Virginia Polytechnic Institute and State University)

  • Siwen Wang

    (Virginia Polytechnic Institute and State University)

  • Luke E. K. Achenie

    (Virginia Polytechnic Institute and State University)

  • Hongliang Xin

    (Virginia Polytechnic Institute and State University)

Abstract

Despite recent advances of data acquisition and algorithms development, machine learning (ML) faces tremendous challenges to being adopted in practical catalyst design, largely due to its limited generalizability and poor explainability. Herein, we develop a theory-infused neural network (TinNet) approach that integrates deep learning algorithms with the well-established d-band theory of chemisorption for reactivity prediction of transition-metal surfaces. With simple adsorbates (e.g., *OH, *O, and *N) at active site ensembles as representative descriptor species, we demonstrate that the TinNet is on par with purely data-driven ML methods in prediction performance while being inherently interpretable. Incorporation of scientific knowledge of physical interactions into learning from data sheds further light on the nature of chemical bonding and opens up new avenues for ML discovery of novel motifs with desired catalytic properties.

Suggested Citation

  • Shih-Han Wang & Hemanth Somarajan Pillai & Siwen Wang & Luke E. K. Achenie & Hongliang Xin, 2021. "Infusing theory into deep learning for interpretable reactivity prediction," Nature Communications, Nature, vol. 12(1), pages 1-9, December.
  • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-25639-8
    DOI: 10.1038/s41467-021-25639-8
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    Cited by:

    1. Ma, Zhengjing & Mei, Gang, 2022. "A hybrid attention-based deep learning approach for wind power prediction," Applied Energy, Elsevier, vol. 323(C).
    2. Gang Wang & Shinya Mine & Duotian Chen & Yuan Jing & Kah Wei Ting & Taichi Yamaguchi & Motoshi Takao & Zen Maeno & Ichigaku Takigawa & Koichi Matsushita & Ken-ichi Shimizu & Takashi Toyao, 2023. "Accelerated discovery of multi-elemental reverse water-gas shift catalysts using extrapolative machine learning approach," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    3. Hemanth Somarajan Pillai & Yi Li & Shih-Han Wang & Noushin Omidvar & Qingmin Mu & Luke E. K. Achenie & Frank Abild-Pedersen & Juan Yang & Gang Wu & Hongliang Xin, 2023. "Interpretable design of Ir-free trimetallic electrocatalysts for ammonia oxidation with graph neural networks," Nature Communications, Nature, vol. 14(1), pages 1-11, December.

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