有色金属材料与工程2025，Vol.46Issue(6)：18-23,6.DOI:10.13258/j.cnki.nmme.20250219001
机器学习辅助的过渡金属掺杂MoS2材料结构筛选:从单原子到双原子掺杂
Machine learning assisted material structure screening for transition metal doped MoS2:from single-atom to double-atom doping
摘要
Abstract
In order to accelerate the design of sensing material structures and control of gas sensing performance,high-throughput computing combined with machine learning was used to study the structure-activity relationship between transition metal doped MoS2 gas sensing materials and adsorbed gases.By constructing single atom and double atom transition metal doped MoS2 systems(TM-MoS2,TM2-MoS2,TM=V,Mn,Co,Mo,Ru),the adsorption energies of three typical indoor gas pollutants HCHO,NH3,and H2S were calculated based on first principles,and four algorithms were used to construct machine learning regression prediction models.The use of a stepwise filtering feature engineering strategy to select a reasonable set of features has improved the generalization ability of the machine learning model.The results indicate that the XGBoost algorithm is the best,with R2 values of 0.91 and 0.78 on the training and testing sets,and RMSE values of 0.15 and 0.34 eV,respectively.Use SHAP to analyze the contribution of each feature to the regression model,where Ng and εd have the highest contribution values,and feature Dg has the smallest contribution.关键词
机器学习/金属掺杂/气体吸附/MoS2Key words
machine learning/metal doping/gas adsorption/MoS2分类
通用工业技术引用本文复制引用
张锦,徐京城..机器学习辅助的过渡金属掺杂MoS2材料结构筛选:从单原子到双原子掺杂[J].有色金属材料与工程,2025,46(6):18-23,6.基金项目
东方科软(北京)科技有限公司横向项目(H-2023-369-039) （北京）
