基于机器学习方法的二维材料带隙预测

doi:10.12066/j.issn.1007-2861.2089

Abstract

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 G₀W₀are 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

CLC Number:

TB39
O469

YOU Yang, DU Wan, LI Weiju, CHEN Jingzhe. Two-dimensional material band gap prediction based on machine learning method[J]. Journal of Shanghai University（Natural Science Edition）, 2020, 26(5): 824-833.

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URL: https://www.journal.shu.edu.cn/EN/10.12066/j.issn.1007-2861.2089

https://www.journal.shu.edu.cn/EN/Y2020/V26/I5/824

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Two-dimensional material band gap prediction based on machine learning method

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