Discovery of Lead‐Free Perovskites for High‐Performance Solar Cells via Machine Learning: Ultrabroadband Absorption, Low Radiative Combination, and Enhanced Thermal Conductivities. Issue 4 (14th December 2021)
- Record Type:
- Journal Article
- Title:
- Discovery of Lead‐Free Perovskites for High‐Performance Solar Cells via Machine Learning: Ultrabroadband Absorption, Low Radiative Combination, and Enhanced Thermal Conductivities. Issue 4 (14th December 2021)
- Main Title:
- Discovery of Lead‐Free Perovskites for High‐Performance Solar Cells via Machine Learning: Ultrabroadband Absorption, Low Radiative Combination, and Enhanced Thermal Conductivities
- Authors:
- Cai, Xia
Zhang, Yiming
Shi, Zejiao
Chen, Ying
Xia, Yujie
Yu, Anran
Xu, Yuanfeng
Xie, Fengxian
Shao, Hezhu
Zhu, Heyuan
Fu, Desheng
Zhan, Yiqiang
Zhang, Hao - Abstract:
- Abstract: 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 A2 BB′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 A2 BB′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 ((CH3 NH3 )2 AgGaBr6, (CH3 NH3 )2 AgInBr6 and (C2 NH6 )2 AgInBr6 ) 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 CH3 NH3 PbI3 and the calculated thermal conductivities are 5.04, 4.39 and 5.16 Wm −1 K −1, respectively, with DebyeAbstract: 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 A2 BB′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 A2 BB′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 ((CH3 NH3 )2 AgGaBr6, (CH3 NH3 )2 AgInBr6 and (C2 NH6 )2 AgInBr6 ) 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 CH3 NH3 PbI3 and the calculated thermal conductivities are 5.04, 4.39 and 5.16 Wm −1 K −1, respectively, with Debye temperatures larger than 500 K, beneficial for suppression of both nonradiative combination and heat‐induced degradation. Abstract : A multi‐step screening scheme is proposed to accelerate the discovery of potential perovskite A2 BB′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 −1 K −1, respectively. … (more)
- Is Part Of:
- Advanced science. Volume 9:Issue 4(2022)
- Journal:
- Advanced science
- Issue:
- Volume 9:Issue 4(2022)
- Issue Display:
- Volume 9, Issue 4 (2022)
- Year:
- 2022
- Volume:
- 9
- Issue:
- 4
- Issue Sort Value:
- 2022-0009-0004-0000
- Page Start:
- n/a
- Page End:
- n/a
- Publication Date:
- 2021-12-14
- Subjects:
- density‐functional theory -- hybrid organic–inorganic perovskites -- lead‐free double perovskites -- machine learning -- photovoltaics
Science -- Periodicals
505 - Journal URLs:
- http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)2198-3844 ↗
http://onlinelibrary.wiley.com/ ↗ - DOI:
- 10.1002/advs.202103648 ↗
- Languages:
- English
- ISSNs:
- 2198-3844
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 27123.xml