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To address surface reaction network complexity using scaling relations machine learning and DFT calculations

Author

Listed:
  • Zachary W. Ulissi

    (SUNCAT Center for Interface Science and Catalysis, Stanford University)

  • Andrew J. Medford

    (School of Chemical and Biomolecular Engineering, Georgia Institute of Technology)

  • Thomas Bligaard

    (SUNCAT Center for Interface Science and Catalysis, SLAC National Accelerator Laboratory)

  • Jens K. Nørskov

    (SUNCAT Center for Interface Science and Catalysis, Stanford University)

Abstract

Surface reaction networks involving hydrocarbons exhibit enormous complexity with thousands of species and reactions for all but the very simplest of chemistries. We present a framework for optimization under uncertainty for heterogeneous catalysis reaction networks using surrogate models that are trained on the fly. The surrogate model is constructed by teaching a Gaussian process adsorption energies based on group additivity fingerprints, combined with transition-state scaling relations and a simple classifier for determining the rate-limiting step. The surrogate model is iteratively used to predict the most important reaction step to be calculated explicitly with computationally demanding electronic structure theory. Applying these methods to the reaction of syngas on rhodium(111), we identify the most likely reaction mechanism. Propagating uncertainty throughout this process yields the likelihood that the final mechanism is complete given measurements on only a subset of the entire network and uncertainty in the underlying density functional theory calculations.

Suggested Citation

  • Zachary W. Ulissi & Andrew J. Medford & Thomas Bligaard & Jens K. Nørskov, 2017. "To address surface reaction network complexity using scaling relations machine learning and DFT calculations," Nature Communications, Nature, vol. 8(1), pages 1-7, April.
  • Handle: RePEc:nat:natcom:v:8:y:2017:i:1:d:10.1038_ncomms14621
    DOI: 10.1038/ncomms14621
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    Cited by:

    1. Shunsaku Yasumura & Kenichiro Saita & Takumi Miyakage & Ken Nagai & Kenichi Kon & Takashi Toyao & Zen Maeno & Tetsuya Taketsugu & Ken-ichi Shimizu, 2023. "Designing main-group catalysts for low-temperature methane combustion by ozone," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    2. 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.
    3. Xiyang Wang & Qilei Yang & Xinbo Li & Zhen Li & Chuan Gao & Hui Zhang & Xuefeng Chu & Carl Redshaw & Shucheng Shi & Yimin A. Wu & Yongliang Ma & Yue Peng & Junhua Li & Shouhua Feng, 2024. "Exploring the dynamic evolution of lattice oxygen on exsolved-Mn2O3@SmMn2O5 interfaces for NO Oxidation," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
    4. 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.
    5. Damilola Ologunagba & Shyam Kattel, 2020. "Machine Learning Prediction of Surface Segregation Energies on Low Index Bimetallic Surfaces," Energies, MDPI, vol. 13(9), pages 1-13, May.

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