Feature Analysis: Revealing the Importance of Bulk Nearest-Neighbor Distance Feature for CO Adsorption Energy on Bimetallic Alloys

An effective machine learning model FAIR (feature analysis based on linear regression) of high interpretation was proposed to predict CO adsorption energy on 105 kinds of bimetallic alloys with high accuracy (root-mean-square error (RMSE) 0.17 eV and R2 0.97). We assessed 524 288 kinds of combinations as input features based on 19 primary features from elemental features and surface electronic features of bimetallic alloys. The results demonstrated that the model not only displayed excellent prediction performance but also captured the hidden physical meaning, implying that the distance between nearest atoms in the bulk of the element in the first layer is the most important feature that determines the accuracy of models; in addition, the emergence of d band center also tends to make some improvements. Furthermore, the FAIR model could be independent of massive amount of database, when the number of train database is far less than that of test database with the ratio of only 3:7, the RMSE still reached 0.13 eV. Taking CO2 electrochemical reduction reaction into account, several promising alloys, especially Au/Rh/Au(111), Au/Pt/Au(111), and Au/Ni/Au(111), are screened as promising candidates capable of transforming CO2 into valuable hydrocarbons based on reasonable CO adsorption energy range. Besides, the FAIR model as an effective tool for property prediction and feature analysis could also be applied to any system with explicit primary features and target.