Prof. Tao Wang’s group at the Center of Artificial Photosynthesis for Solar Fuels at Westlake University (CAP for Solar Fuels @Westlake) recently published a research paper in Nature Communications, which is titled “Resolving chemical-motif similarity with enhanced atomic structure representations for accurately predicting descriptors at metallic interfaces.”
This work has successfully tackled the challenge of resolving chemical-motif similarity in highly complex catalytic systems by integrating equivariant message-passing-enhanced atomic structure representation. The team developed an equivariant graph neural network (equivGNN) model, achieving mean absolute errors of <0.09 eV for different descriptors at metallic interfaces. The model’s high prediction accuracy and ease of implementation across various systems demonstrate its robustness and potentially broad applicability, laying a reasonable basis for accelerating catalyst design.
Assistant Researcher Dr. Cheng Cai from the CAP for Solar Fuels @Westlake is the first author, and Professor Tao Wang is the corresponding author.