Research highlight: First‐Principles Density Functional Theory and Machine Learning Technique for the Prediction of Water Adsorption Site on PtPd‐Based High‐Entropy‐Alloy Catalysts

This work presented screening high-entropy-alloys based on FCC (111) surface for H2O activation during the water-gas-shift reaction using density functional theory (DFT) calculation and machine learning. H2O favors adsrobing on atop sites on PtPd-based high-entropy-alloy surfaces. Electronic properties, d-band center and Bader charge analysis, of atop-site PtPd-based high-entropy-alloy surfaces depend on neighboring atoms. Machine-learning gaussian process regression can predict adsorption energy of H2O with RMSE = 0.09 (testing data) and 0.17 (validation/untrained data). PtPdRhAgCo, PtPdRuAgCo, PtPdRhAgFe, and PtPdRuAgFe are the potential candidate having Co, Ru, and Fe as active sites for H2O adsorption.

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