Bandgap tuning strategy by cations and halide ions of lead halide perovskites learned from machine learning. Issue 26 (27th April 2021)

• Record Type: Journal Article
• Authors: Li, Yaoyao; Lu, Yao; Huo, Xiaomin; Wei, Dong; Meng, Juan; Dong, Jie; Qiao, Bo; Zhao, Suling; Xu, Zheng; Song, Dandan
• Is Part Of: RSC advances. Volume 11:Issue 26(2021)
• Page Start: 15688
• Page End: 15694
• Publication Date: 2021-04-27
• Subjects: Chemistry -- Periodicals; 540.5
• DOI: 10.1039/d1ra03117a
• Languages: English
• ISSNs: 2046-2069

Abstract

Abstract : Bandgap engineering of lead halide perovskite materials is critical to achieve highly efficient and stable perovskite solar cells and color tunable stable perovskite light-emitting diodes. Abstract : Bandgap engineering of lead halide perovskite materials is critical to achieve highly efficient and stable perovskite solar cells and color tunable stable perovskite light-emitting diodes. Herein, we propose the use of machine learning as a tool to predict the bandgap of the perovskite materials from their compositions. By learning from the experimental results, machine learning algorithms present reliable performance in predicting the bandgap of the lead halide perovskites. The linear regression model can be used to manually predict the bandgap of the perovskite with the formula of Cs a FA b MA(1− a − b ) Pb(Cl x Br y I(1− x − y ) )3 (FA = formamidinium, MA = methylammonium). The neural network (NN) algorithm, which takes the interplay of cations and halide ions into account in predicting the bandgap, presents higher accuracy (with a RMSE of 0.05 eV and a Pearson coefficient larger than 0.99). Furthermore, the compositions of the mixed halide perovskites with desirable bandgaps and high iodide ratio for suppressing halide segregation are predicted by NN algorithm. These results highlight the power of machine learning in predicting the bandgap of the perovskites from their compositions and provide bandgap tuning directions for experiments.