In‐Depth Discussion on Electrocatalytic Barrier with Electron Structure of High‐Entropy Alloy Predicted by Transfer Learning and Neural Networks

Abstract Alloy electrodes, beneficial from excellent stability, are considered suitable for industrial applications, hence exploring alloy catalysts with low reaction barriers will bring innovative scientific understanding and enormous economic benefits. Recently, material informatics emerges as an efficient method in the research and development of new materials through diverse candidates, however, collecting a large amount of material characterization and simulation data still faces numerous difficulties. To tackle this issue, combining the topological structure of materials, the convolutional neural network framework developed in this article first achieves the density of states prediction of active sites on the alloy surface, based on which the adsorption energy of different reactants is obtained. Benefited by electronic structure, this model exhibits excellent predictive performance with a mean absolute error of 0.124 eV, and transferability with fast convergence under dozens transferred data to complete the extension for high entropy alloys and reactants. Based on this massive predictive data, high entropy alloy catalysts with excellent low reaction barrier have been discovered, and several catalytic theories, like scaling relations, d‐band center theory, high‐entropy effects and synergistic catalysis, have been validated and improved.