Data-driven discovery of biaxially strained single atoms array for hydrogen production
Tao Zhang, Qitong Ye, Yipu Liu, Qingyi Liu, Zengyu Han, Dongshuang Wu, Zhiming Chen, Yue Li, Hong Jin Fan
Abstract
The structure-performance relationship for single atom catalysts has remained unclear due to the averaged coordination information obtained from most single-atom catalysts. Periodic array of single atoms may provide a platform to tackle this inaccuracy. Here, we develop a data-driven approach by incorporating high-throughput density functional theory computations and machine learning to screen candidates based on a library of 1248 sites from single atoms array anchored on biaxial-strained transition metal dichalcogenides. Our screening results in Au atom anchored on biaxial-strained MoSe2 surface via Au-Se3 bonds. Machine learning analysis identifies four key structural features by classifying the ΔGH* data. We show that the average band center of the adsorption sites can be a predictor for hydrogen adsorption energy. This prediction is validated by experiments which show single-atom Au array anchored on biaxial-strained MoSe2 archives 1000 hour-stability at 800 mA cm-2 towards acidic hydrogen evolution. Moreover, active hotspot consisting of Au atoms array and the neighboring Se atoms is unraveled for enhanced activity. The structure-performance relationship of single-atom catalysts remains unclear. Here a data-driven approach with high-throughput DFT and machine learning is used to screen 1248 single atoms arrays, to provide a better understanding of the hydrogen evolution reaction mechanism.