Abstract:

Machine learning (ML) algorithm and the traditional density functional theory (DFT) are combined to study the band gap of two-dimensional metal compounds, and as a result, a simple and effective model which is more cost-effective than the traditional quantum calculation method is established. Results of general gradient approximation-Perdew-Burke-Ernzerhof (GGA-PBE) and  G₀W₀ are taken as reference and a two-dimensional material data set with chemical formula MX₂ is investigated. Least absolute shrinkage and selection operator (LASSO), support vector machine regression (SVR) and gradient boosting regressor (GBR) and other machine learning methods are used to build a band gap prediction model. Among these models, it is found that the SVR model based on linear kernel function and LASSO model both can give a good prediction result, the mean absolute error (MAE) of training model is 0.34 eV and MAE of testing set is 0.5 eV. Thus, for the prediction of two-dimensional material band gap, the feature parameter set adopted by us has a certain completeness and rationality, which has a certain reference value for the preliminary prediction for the band gap of new materials.

Key words: two-dimensional materials, first principles, machine learning, band gap