期刊
JOURNAL OF PHYSICAL CHEMISTRY C
卷 126, 期 31, 页码 13053-13061出版社
AMER CHEMICAL SOC
DOI: 10.1021/acs.jpcc.2c04725
关键词
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资金
- ERC [101020369]
- Spanish Ministry of Science and Innovation (MICINN) [PID2021-128569NB-I00, RED2018-102815-T, RED2018-102331-T, RyC-2017-23500]
- EPSRC vacation internship program
- Generalitat Valenciana [101020369]
- EPSRC
- [PROMETEO/2020/077]
- European Research Council (ERC) [101020369] Funding Source: European Research Council (ERC)
This study created a dataset of 269 perovskite solar cells and proposed a predictive machine learning model for screening candidate hole-transporting materials, accurately predicting cell performance, analyzing the impact of data biases on the model, and determining the correlation between chemical fragments and cell efficiency.
We have created a dataset of 269 perovskite solar cells, containing information about their perovskite family, cell architecture, and multiple hole-transporting materials features, including fingerprints, additives, and structural and electronic features. We propose a predictive machine learning model that is trained on these data and can be used to screen possible candidate hole-transporting materials. Our approach allows us to predict the performance of perovskite solar cells with reasonable accuracy and is able to successfully identify most of the top-performing and lowest-performing hole-transporting materials in the dataset. We discuss the effect of data biases on the distribution of perovskite families/architectures on the model's accuracy and offer an analysis with a subset of the data to accurately study the effect of the hole-transporting material on the solar cell performance. Finally, we discuss some chemical fragments, like arylamine and aryloxy groups, which present a relatively large positive correlation with the efficiency of the cell, whereas other groups, like thiophene groups, display a negative correlation with power conversion efficiency (PCE).
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