期刊
ENERGY & ENVIRONMENTAL MATERIALS
卷 6, 期 1, 页码 -出版社
WILEY
DOI: 10.1002/eem2.12259
关键词
crystal graph convolutional neural network; deep learning; hydrogen evolution reaction; two-dimensional (2D) material
Researchers have used crystal graph convolutional neural networks in a deep learning method to accelerate the discovery of high-performance two-dimensional hydrogen evolution reaction catalysts. By considering all active sites and predicting their adsorption energies, they have screened out 38 high-performance catalysts and determined the potential strongest adsorption sites.
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.
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