Data-driven design of high-performance MASnxPb1-xI3 perovskite materials by machine learning and experimental realization

• Published in: Light, science & applications Vol. 11; no. 1; pp. 234 - 12
• Main Authors: Cai, Xia, Liu, Fengcai, Yu, Anran, Qin, Jiajun, Hatamvand, Mohammad, Ahmed, Irfan, Luo, Jiayan, Zhang, Yiming, Zhang, Hao, Zhan, Yiqiang
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 26.07.2022; Springer Nature B.V; Nature Publishing Group
• Subjects: 639/624/1075/524; 639/766/1130; Experiments; Lasers; Lead; Learning algorithms; Machine learning; Microwaves; Optical and Electronic Materials; Optical Devices; Optics; Optimization; Photonics; Photovoltaic cells; Physics; Physics and Astronomy; RF and Optical Engineering; Short-circuit current
• ISSN: 2047-7538; 2095-5545; 2047-7538
• DOI: 10.1038/s41377-022-00924-3

The photovoltaic performance of perovskite solar cell is determined by multiple interrelated factors, such as perovskite compositions, electronic properties of each transport layer and fabrication parameters, which makes it rather challenging for optimization of device performances and discovery of underlying mechanisms. Here, we propose and realize a novel machine learning approach based on forward-reverse framework to establish the relationship between key parameters and photovoltaic performance in high-profile MASn x Pb 1-x I 3 perovskite materials. The proposed method establishes the asymmetrically bowing relationship between band gap and Sn composition, which is precisely verified by our experiments. Based on the analysis of structural evolution and SHAP library, the rapid-change region and low-bandgap plateau region for small and large Sn composition are explained, respectively. By establishing the models for photovoltaic parameters of working photovoltaic devices, the deviation of short-circuit current and open-circuit voltage with band gap in defective-zone and low-bandgap-plateau regions from Shockley-Queisser theory is captured by our models, and the former is due to the deep-level traps formed by crystallographic distortion and the latter is due to the enhanced susceptibility by increased Sn 4+ content. The more difficulty for hole extraction than electron is also concluded in the models and the prediction curve of power conversion efficiency is in a good agreement with Shockley-Queisser limit. With the help of search and optimization algorithms, an optimized Sn:Pb composition ratio near 0.6 is finally obtained for high-performance perovskite solar cells, then verified by our experiments. Our constructive method could also be applicable to other material optimization and efficient device development. The forward-reverse framework based on machine learning for MASn x Pb 1-x I 3 perovskite solar cells is reported. The practicability of bandgap model revealing asymmetrically-bowing shape and optimized Sn:Pb ratio are verified by experiments.