Li Yaoyao, Lu Yao, Huo Xiaomin, Wei Dong, Meng Juan, Dong Jie, Qiao Bo, Zhao Suling, Xu Zheng, Song Dandan
Key Laboratory of Luminescence and Optical Information, Beijing Jiaotong University, Ministry of Education Beijing 100044 China
Institute of Optoelectronics Technology, Beijing Jiaotong University Beijing 100044 China.
RSC Adv. 2021 Apr 27;11(26):15688-15694. doi: 10.1039/d1ra03117a. eCollection 2021 Apr 26.
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 FA MAPb(Cl Br I) (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.
卤化铅钙钛矿材料的带隙工程对于实现高效稳定的钙钛矿太阳能电池和颜色可调的稳定钙钛矿发光二极管至关重要。在此，我们提出使用机器学习作为工具，从其组成预测钙钛矿材料的带隙。通过从实验结果中学习，机器学习算法在预测卤化铅钙钛矿的带隙方面表现出可靠的性能。线性回归模型可用于通过Cs FA MAPb(Cl Br I)（FA = 甲脒，MA = 甲基铵）公式手动预测钙钛矿的带隙。在预测带隙时考虑阳离子和卤离子相互作用的神经网络（NN）算法具有更高的准确性（RMSE为0.05 eV，皮尔逊系数大于0.99）。此外，通过NN算法预测了具有理想带隙和高碘化物比例以抑制卤化物偏析的混合卤化物钙钛矿的组成。这些结果突出了机器学习在从其组成预测钙钛矿带隙方面的能力，并为实验提供了带隙调节方向。
