Data-Driven Analysis of Hole-Transporting Materials for Perovskite Solar Cells Performance

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.

Access the full-text of this item by clicking on the Open Access link.


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:
Related URLs: