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 func...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(E_(ads))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(ΔG_(H^(*)))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.展开更多
基金the Second Century Fund(C2F),Chulalongkorn UniversityThailand Science Research and Innovation Fund Chulalongkorn University(No.IND_FF_68_054_2100_009)National Science and Technology Development Agency,Thailand,Hub of Knowledge funding,and the Mid-Career Research Grant 2024,National Research Council of Thailand(No.N42A670295).
文摘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(E_(ads))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(ΔG_(H^(*)))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.
