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Official PDF TranslationInt. Journal of Minerals, Metallurgy and Materials (矿物冶金与材料学报)

Machine learning-accelerated density functional theory optimization of PtPd-based high-entropy alloys for hydrogen evolution catalysis

Authors: Patcharaporn Khajondetchairit; Siriwimol Somdee; Tinnakorn Saelee; Annop Ektarawong; Björn Alling; Piyasan Praserthdam; Meena Rittiruam; Supareak Praserthdam

DOI: 10.1007/s12613-025-3173-zStatus: Verified Translated Edition
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Key Findings in This Report

• PtPdRuCoNi(111) identified as most promising HEA catalyst for HER with ΔGH* of −0.03 eV, outperforming Pt(111). • Machine learning (XGBR) accelerates DFT screening with high accuracy (RMSE = 0.128 eV), reducing computational cost. • Adsorption energies between −0.50 and −0.60 eV and d-band center of −1.85 eV indicate enhanced catalytic activity. • Study demonstrates potential of HEAs for efficient and sustainable hydrogen production, addressing cost and scarcity of noble metals.