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Journal of Shanghai University >

2020 , Vol. 26 >Issue 5: 824 - 833

DOI: https://doi.org/10.12066/j.issn.1007-2861.2089

Research Articles

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

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  • 1. College of Sciences, Shanghai University, Shanghai 200444, China
    2. International Centre for Quantum and Molecular Structures, Shanghai University, Shanghai 200444, China
    3. Materials Genome Institute, Shanghai University, Shanghai 200444, China

Received date: 2018-09-12

  Online published: 2018-09-21

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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

Cite this article

YOU Yang, DU Wan, LI Weiju, CHEN Jingzhe . Two-dimensional material band gap prediction based on machine learning method[J]. Journal of Shanghai University, 2020 , 26(5) : 824 -833 . DOI: 10.12066/j.issn.1007-2861.2089

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