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Resolving chemical-motif similarity with enhanced atomic structure representations for accurately predicting descriptors at metallic interfaces

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  • Cheng Cai

    (600 Dunyu Road
    Zhejiang Baima Lake Laboratory
    18 Shilongshan Road)

  • Tao Wang

    (600 Dunyu Road
    Zhejiang Baima Lake Laboratory
    18 Shilongshan Road)

Abstract

Accurately predicting catalytic descriptors with machine learning (ML) methods is significant to achieving accelerated catalyst design, where a unique representation of the atomic structure of each system is the key to developing a universal, efficient, and accurate ML model that is capable of tackling diverse degrees of complexity in heterogeneous catalysis scenarios. Herein, we integrate equivariant message-passing-enhanced atomic structure representation to resolve chemical-motif similarity in highly complex catalytic systems. Our developed equivariant graph neural network (equivGNN) model achieves mean absolute errors

Suggested Citation

  • Cheng Cai & Tao Wang, 2025. "Resolving chemical-motif similarity with enhanced atomic structure representations for accurately predicting descriptors at metallic interfaces," Nature Communications, Nature, vol. 16(1), pages 1-10, December.
    • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-63860-x
    DOI: 10.1038/s41467-025-63860-x
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    References listed on IDEAS

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