Application of machine learning in high-throughput screening of binary alloys for the hydrogenation of benzene

Abstract The hydrogenation of benzene is a key reaction in industry, and binary alloys are promising candidates for improving the catalytic efficiency of this process. In this study, the adsorption energies of benzene and hydrogen over random 150 alloys are determined using density functional theory (DFT) calculation, and varied physical properties of alloys are used as descriptors. Four machine learning (ML) models, light gradient boosting machine (LGBM), extreme gradient boosting (XGBT), multilayer perceptron (MLP) and support vector machine (SVM) are employed to predict the adsorption energies. After feature selection and parameter optimization, LGBM model shows the highest prediction accuracy, with correlation coefficient (R²) and root mean square error (RMSE) of 0.813 and 0.415 eV for benzene, as well as 0.874 and 0.176 eV for hydrogen. Therefore, LGBM model is selected to predict the adsorption energies of benzene and hydrogen (ΔEB and ΔEH), and Cu2Ni2 has excellent ΔEB and ΔEH of -4.97 and − 1.81 eV.