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Bayesian learning of chemisorption for bridging the complexity of electronic descriptors

Author

Listed:
  • Siwen Wang

    (Virginia Polytechnic Institute and State University)

  • Hemanth Somarajan Pillai

    (Virginia Polytechnic Institute and State University)

  • Hongliang Xin

    (Virginia Polytechnic Institute and State University)

Abstract

Building upon the d-band reactivity theory in surface chemistry and catalysis, we develop a Bayesian learning approach to probing chemisorption processes at atomically tailored metal sites. With representative species, e.g., *O and *OH, Bayesian models trained with ab initio adsorption properties of transition metals predict site reactivity at a diverse range of intermetallics and near-surface alloys while naturally providing uncertainty quantification from posterior sampling. More importantly, this conceptual framework sheds light on the orbitalwise nature of chemical bonding at adsorption sites with d-states characteristics ranging from bulk-like semi-elliptic bands to free-atom-like discrete energy levels, bridging the complexity of electronic descriptors for the prediction of novel catalytic materials.

Suggested Citation

  • Siwen Wang & Hemanth Somarajan Pillai & Hongliang Xin, 2020. "Bayesian learning of chemisorption for bridging the complexity of electronic descriptors," Nature Communications, Nature, vol. 11(1), pages 1-7, December.
    • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-19524-z
    DOI: 10.1038/s41467-020-19524-z
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    Cited by:

    1. Qiang Gao & Hemanth Somarajan Pillai & Yang Huang & Shikai Liu & Qingmin Mu & Xue Han & Zihao Yan & Hua Zhou & Qian He & Hongliang Xin & Huiyuan Zhu, 2022. "Breaking adsorption-energy scaling limitations of electrocatalytic nitrate reduction on intermetallic CuPd nanocubes by machine-learned insights," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    2. Chao Liang & Yilimiranmu Rouzhahong & Caiyuan Ye & Chong Li & Biao Wang & Huashan Li, 2023. "Material symmetry recognition and property prediction accomplished by crystal capsule representation," Nature Communications, Nature, vol. 14(1), pages 1-11, 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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