del Cueto, Marcos, Rawski-Furman, Charles, Arago, Juan, Orti, Enrique and Troisi, Alessandro 
ORCID: 0000-0002-5447-5648
(2022)
Data-Driven Analysis of Hole-Transporting Materials for Perovskite Solar Cells Performance.
JOURNAL OF PHYSICAL CHEMISTRY C, 126 (31).
pp. 13053-13061.
Abstract
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).
| Item Type: | Article |
|---|---|
| Divisions: | Faculty of Science and Engineering > School of Physical Sciences |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 16 Aug 2022 10:14 |
| Last Modified: | 18 Jan 2023 20:47 |
| DOI: | 10.1021/acs.jpcc.2c04725 |
| Open Access URL: | https://doi.org/10.1021/acs.jpcc.2c04725 |
| Related URLs: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3161372 |
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