Employing the Interpretable Ensemble Learning Approach to Predict the Bandgaps of the Halide Perovskites

Author:

Ren Chao1234ORCID,Wu Yiyuan123,Zou Jijun12ORCID,Cai Bowen15

Affiliation:

1. Jiangxi Province Key Laboratory of Nuclear Physics and Technology, East China University of Technology, Nanchang 330013, China

2. Engineering Research Center of Nuclear Technology Application, East China Institute of Technology, Ministry of Education, Nanchang 330013, China

3. Jiangxi Engineering Laboratory on Radioactive Geoscience and Big Data Technology, East China University of Technology, Nanchang 330013, China

4. School of Information Engineering, East China University of Technology, Nanchang 330013, China

5. School of Nuclear Science and Engineering, East China University of Technology, Nanchang 330013, China

Abstract

Halide perovskite materials have broad prospects for applications in various fields such as solar cells, LED devices, photodetectors, fluorescence labeling, bioimaging, and photocatalysis due to their bandgap characteristics. This study compiled experimental data from the published literature and utilized the excellent predictive capabilities, low overfitting risk, and strong robustness of ensemble learning models to analyze the bandgaps of halide perovskite compounds. The results demonstrate the effectiveness of ensemble learning decision tree models, especially the gradient boosting decision tree model, with a root mean square error of 0.090 eV, a mean absolute error of 0.053 eV, and a determination coefficient of 93.11%. Research on data related to ratios calculated through element molar quantity normalization indicates significant influences of ions at the X and B positions on the bandgap. Additionally, doping with iodine atoms can effectively reduce the intrinsic bandgap, while hybridization of the s and p orbitals of tin atoms can also decrease the bandgap. The accuracy of the model is validated by predicting the bandgap of the photovoltaic material MASn1−xPbxI3. In conclusion, this study emphasizes the positive impact of machine learning on material development, especially in predicting the bandgaps of halide perovskite compounds, where ensemble learning methods demonstrate significant advantages.

Funder

Key Research and Development Program of Jiangxi province

Jiangxi Nuclear Geology Data Science and System Engineering Technology Research Center Open Fund

Publisher

MDPI AG

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