Data-driven discovery of biaxially strained single atoms array for hydrogen production.
basic_science · Level V
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- Record sourced from PubMed, PMID 40240379.
- Also identified by DOI 10.1038/s41467-025-59053-1 and PMC identifier 12003809.
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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 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.