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Machine learning-accelerated density functional theory optimization of PtPd-based high-entropy alloys for hydrogen evolution catalysis
Author(s)
Khajondetchairit, Patcharaporn
Somdee, Siriwimol
Saelee, Tinnakorn
Ektarawong, Annop
Alling, Björn
Praserthdam, Piyasan
Rittiruam, Meena
Praserthdam, Supareak
Date Issued
November 1, 2025
Type
Article
Abstract
High-entropy alloys (HEAs) have emerged as promising catalysts for the hydrogen evolution reaction (HER) due to their compositional diversity and synergistic effects. In this study, machine learning-accelerated density functional theory (DFT) calculations were employed to assess the catalytic performance of PtPd-based HEAs with the formula PtPdXYZ (X, Y, Z = Fe, Co, Ni, Cu, Ru, Rh, Ag, Au; X ≠ Y ≠ Z). Among 56 screened HEA(111) surfaces, PtPdRuCoNi(111) was identified as the most promising, with adsorption energies (Eads) between −0.50 and −0.60 eV and high d-band center of −1.85 eV, indicating enhanced activity. This surface showed the hydrogen adsorption free energy (ΔGH*) of −0.03 eV for hydrogen adsorption, outperforming Pt(111) by achieving a better balance between adsorption and desorption. Machine learning models, particularly extreme gradient boosting regression (XGBR), significantly reduced computational costs while maintaining high accuracy (root-mean-square error, RMSE = 0.128 eV). These results demonstrate the potential of HEAs for efficient and sustainable hydrogen production.
Citation
International Journal of Minerals Metallurgy and Materials, 32(11), 2777-2785, 2025