A method for identifying catalyst active sites based on deep learning

In order to improve the accuracy and interpretability of catalyst active site identification, multi-scale graph neural network was used to study the application of graph convolutional network and attention mechanism in capturing catalyst surface structural features. The effect of fusion of different scale information on the model performance is analyzed. The results show that the multi-scale graph neural network outperforms the traditional method in all evaluation indexes, especially the F1 score, which shows its potential in catalyst design and optimization.