Deep Learning Accelerates the Discovery of Two‐Dimensional Catalysts for Hydrogen Evolution Reaction

Two‐dimensional materials with active sites are expected to replace platinum as large‐scale hydrogen production catalysts. However, the rapid discovery of excellent two‐dimensional hydrogen evolution reaction catalysts is seriously hindered due to the long experiment cycle and the huge cost of high‐throughput calculations of adsorption energies. Considering that the traditional regression models cannot consider all the potential sites on the surface of catalysts, we use a deep learning method with crystal graph convolutional neural networks to accelerate the discovery of high‐performance two‐dimensional hydrogen evolution reaction catalysts from two‐dimensional materials database, with the prediction accuracy as high as 95.2%. The proposed method considers all active sites, screens out 38 high performance catalysts from 6,531 two‐dimensional materials, predicts their adsorption energies at different active sites, and determines the potential strongest adsorption sites. The prediction accuracy of the two‐dimensional hydrogen evolution reaction catalysts screening strategy proposed in this work is at the density‐functional‐theory level, but the prediction speed is 10.19 years ahead of the high‐throughput screening, demonstrating the capability of crystal graph convolutional neural networks‐deep learning method for efficiently discovering high‐performance new structures over a wide catalytic materials space.