燃料化学学报（中英文）2025，Vol.53Issue(6)：895-905,中插1-中插7,18.DOI:10.1016/S1872-5813(24)60525-6
机器学习辅助过渡金属磷化物电解水制氢催化剂的设计研究
Machine learning assisted study of the transition metal phosphides catalyst for the water electrolysis to produce hydrogen
摘要
Abstract
In recent years,hydrogen production by water electrolysis has become an important strategy of energy transformation in China,where the design of efficient catalysts for hydrogen evolution reaction(HER)is a key issue.In this regard,transition metal phosphides(TMPs)are considered important non-precious metal catalysts in water electrolysis owing to their low price and high hydrogen production efficiency.However,experimental screening of highly active TMPs catalysts is time-consuming and challenging.This study provides a simple and effective method for rapidly screening highly efficient HER electrocatalysts based on machine learning and big-data analysis.Four machine learning algorithms,namely support vector regression(SVR),K-nearest neighbour(KNN),random forest regression(RF)and extreme gradient boosting(XGBoost),were developed to predict the catalytic performance of various transition metal phosphides reported in the literature in HER.After evaluating the four algorithms by RMSE and R2,it was found that the RF algorithm has excellent prediction ability for overpotential,while the XGBoost algorithm predicts better for the Tafel slope.It is concluded that the contents of Ni,Co and Fe have a significant influence on the catalytic performance and highly active catalysts may be prepared by fine adjustment of their contents in the future.关键词
电解水/氢能/机器学习/过渡金属磷化物Key words
water electrocatalysis/hydrogen evolution reaction/machine learning/transition metal phosphides分类
化学化工引用本文复制引用
李宇明,徐砚文,王焕焕,朱海若,朱梓翀,马丽娜,王雅君..机器学习辅助过渡金属磷化物电解水制氢催化剂的设计研究[J].燃料化学学报（中英文）,2025,53(6):895-905,中插1-中插7,18.基金项目
The project was supported by the National Key Research and Development Program of China(2021YFB4000405),National Natural Science Foundation of China(52270115,52236003,21777080),and Carbon Neutral Joint Research Institute Research Project(CNIF20230208). 国家重点研发计划(2021YFB4000405),国家自然科学基金(52270115,52236003,21777080)和碳中和联合研究院自主基金(CNIF20230208)资助 （2021YFB4000405）
