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

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 <0.09 eV for different descriptors at metallic interfaces, including complex adsorbates with more diverse adsorption motifs on ordered catalyst surfaces, adsorption motifs on highly disordered surfaces of high-entropy alloys, and the complex structures of supported nanoparticles. The prediction accuracy and easy implementation attained by our model across various systems demonstrate its robustness and potentially broad applicability, laying a reasonable basis for achieving accelerated catalyst design. Catalytic descriptors are crucial to accelerating catalyst design. Here, the authors develop an equivariant graph neural network to enable robust structure representations and achieve accurate predictions of descriptors across complex catalytic systems.