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 <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.