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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.

The shuttle effect has been a major obstacle to the development of lithium–sulfur batteries. The discovery of new host materials is essential, but lengthy and complex experimental studies are inefficient for the identification of potential host materials. We proposed a machine learning method for the rapid discovery of an AB2-type sulfur host material to suppress the shuttle effect using the 2DMatPedia database, discovering 14 new structures (PdN2, TaS2, PtN2, TaSe2, AgCl2, NbSe2, TaTe2, AgF2, NiN2, AuS2, TmI2, NbTe2, NiBi2, and AuBr2) from 1320 AB2-type compounds. These structures have strong adsorptions of greater than 1.0 eV for lithium polysulfides and appreciable electron-transportation capability, which can serve as the most promising AB2-type host materials in lithium–sulfur batteries. On the basis of a small data set, we successfully predicted Li2S6 adsorption at arbitrary sites on substrate materials using transfer learning, with a considerably low mean absolute error (below 0.05 eV). The proposed data-driven method, as accurate as density functional theory calculations, significantly shortens the research cycle of screening AB2-type sulfur host materials by approximately 8 years. This method provides high-precision and expeditious solutions for other high-throughput calculations and material screenings based on adsorption energy predictions.

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Engineering high-performance cathodes for high energy-density lithium-sulfur (Li-S) batteries are quite significant to achieve commercialization. Here, we develop a graphene oxide scaffold/sulfur composite-encapsulated microcapsule (GSM) for high-performance Li-S battery, which...