Discovery of Lead-Free Perovskites for High-Performance Solar Cells via Machine Learning: Ultrabroadband Absorption, Low Radiative Combination, and Enhanced Thermal Conductivities.
Discovery of Lead-Free Perovskites for High-Performance Solar Cells via Machine Learning: Ultrabroadband Absorption, Low Radiative Combination, and Enhanced Thermal Conductivities.
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通过机器学习发现用于高性能太阳能电池的无铅钙钛矿：超宽带吸收、低辐射组合和增强的热导率
DOI：
10.1002/advs.202103648
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发表时间：
2022-03
期刊：
影响因子：
--
通讯作者：
Zhang H
中科院分区：
文献类型：
--
作者：
Cai X;Zhang Y;Shi Z;Chen Y;Xia Y;Yu A;Xu Y;Xie F;Shao H;Zhu H;Fu D;Zhan Y;Zhang H
Exploring lead‐free candidates and improving efficiency and stability remain the obstacle of hybrid organic‐inorganic perovskite‐based devices commercialization. Traditional trial‐and‐error methods seriously restrict the discovery especially for large search space, complex crystal structure and multi‐objective properties. Here, the authors propose a multi‐step and multi‐stage screening scheme to accelerate the discovery of hybrid organic‐inorganic perovskites A2BB′X6 from a large number of candidates through combining machine learning with high‐throughput calculations for pursuing excellent efficiency and thermal stability in solar cells. Followed by a series of screenings, the structure‐property relationships mapping A2BB′X6 properties are built and the predictions are close to reported experimental results. Successfully, four experimental‐feasibly candidates with good stability, high Debye temperature and suitable band gap are screened out and further verified by density‐functional theory calculations, in which the predicted efficiency for three lead‐free candidates ((CH3NH3)2AgGaBr6, (CH3NH3)2AgInBr6 and (C2NH6)2AgInBr6) achieves 20.6%, 19.9% and 27.6% due to ultrabroadband absorption region ranging from UVC to IRC with excitonic radiative combination rates as low as 10 ps, large or intermediate polarons form with properties similar to CH3NH3PbI3 and the calculated thermal conductivities are 5.04, 4.39 and 5.16 Wm−1K−1, respectively, with Debye temperatures larger than 500 K, beneficial for suppression of both nonradiative combination and heat‐induced degradation. A multi‐step screening scheme is proposed to accelerate the discovery of potential perovskite A2BB′X6 through combining machine learning with high‐throughput calculations for the target of excellent efficiency and thermal stability in solar cells. Among final candidates, the predicted efficiency for three lead‐free candidates achieves 20.6%, 19.9% and 27.6%, and the calculated thermal conductivities are 5.04, 4.39 and 5.16 Wm−1K−1, respectively.
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影响因子：
2.7
作者：
Giannozzi, P.;Andreussi, O.;Baroni, S.
通讯作者：
Baroni, S.
影响因子：
10.8
作者：
Frost JM;Butler KT;Brivio F;Hendon CH;van Schilfgaarde M;Walsh A
通讯作者：
Walsh A
影响因子：
3.7
作者：
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通讯作者：
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影响因子：
8.6
作者：
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通讯作者：
Tayazza, Francesca
DOI：
10.1073/pnas.1719179115
发表时间：
2018-05-22
影响因子：
11.1
作者：
Filip, Marina R.;Giustino, Feliciano
通讯作者：
Giustino, Feliciano