Machine learning accelerated DFT research on platinum-modified amorphous alloy surface catalysts  

作　　者：Xi Zhang Kangpu Li Bo Wen Jiang Ma Dongfeng Diao 

机构地区：[1]Institute of Nanosurface Science and Engineering,Guangdong Provincial Key Laboratory of Micro/Nano,Shenzhen University,Shenzhen 518060,China [2]College of Mechatronics and Control Engineering,Shenzhen University,Shenzhen 518060,China [3]Research Center of Medical Plasma Technology,Shenzhen University,Shenzhen 518060,China

出　　处：《Chinese Chemical Letters》2023年第5期645-649,共5页中国化学快报（英文版）

基　　金：the National Natural Science Foundation(Nos.52275565 and 62104155)of China;Natural Science Foundation of Guangdong Province(No.2022A1515011667);Guangdong Kangyi Special Fund(No.2020KZDZX1173)。

摘　　要：Pt-modified amorphous alloy(Pt@PdNiCuP)catalyst exhibits excellent electro-catalytic activity and high experimental durability for hydrogen evolution reaction(HER).However,the physical origin of the catalytically active remains unclear.In this paper,we constructed a distance contribution descriptor(DCD)for the feature engineering of machine learning(ML)potential,and calculated the Gibbs free energies(ΔGH)of 46,000*H binding sites on the Pt@Pd Ni Cu P surface by ML-accelerated density functional theory(DFT).The relationship betweenΔGHand DCD revealed that in the H-Pt distance region of 2.0-2.5 A where the parabolic tail and disordered scatters coexist,the H-metal bonding configuration is mainly the bridge-or hollow-bonding type.The contribution analysis of DCD indicates that the joint effect of Pt,Pd and Ni atoms determines the catalytical behavior of amorphous alloy,which agrees well with experimental results.By counting atomic percentages in different energy intervals,we obtained the atomic ratio for the best catalytic performance(Pt:Pd:Ni:Cu:P=0.33:0.17:0.155:0.16:0.185).Projected density of states(PDOS)show that H 1s orbital,Pt 5d orbital,and Pd 4d orbital form a bonding state at-2 e V.These results provide new ideas for designing more active amorphous alloy catalysts.

关 键 词：Hydrogen evolution reaction Amorphous alloy Density functional theory Machine learning 

分 类 号：TQ426[化学工程]