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

Cheng Cai and Tao Wang ()
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Cheng Cai: 600 Dunyu Road
Tao Wang: 600 Dunyu Road

Nature Communications, 2025, vol. 16, issue 1, 1-10

Abstract: 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

Date: 2025
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DOI: 10.1038/s41467-025-63860-x

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