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<p>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 MoSe<sub>2</sub> surface via Au-Se<sub>3</sub> bonds. Machine learning analysis identifies four key structural features by classifying the ΔG<sub>H*</sub> 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 MoSe<sub>2</sub> archives 1000 hour-stability at 800 mA cm<sup>-2</sup> towards acidic hydrogen evolution. Moreover, active hotspot consisting of Au atoms array and the neighboring Se atoms is unraveled for enhanced activity.</p>

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Data-driven discovery of biaxially strained single atoms array for hydrogen production

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年4月17日

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  • Tao Zhang,
  • Qitong Ye,
  • Yipu Liu,
  • Qingyi Liu,
  • Zengyu Han,
  • Dongshuang Wu,
  • Zhiming Chen,
  • Yue Li,
  • Hong Jin Fan

摘要

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.