Identification of the dominant recombination process for perovskite solar cells based on machine learning
Le Corre VM, Sherkar TS, Koopmans M, Koster LJA (2021)
Publication Status: Published
Publication Type: Journal article, Online publication
Publication year: 2021
Journal
Publisher: ELSEVIER
Book Volume: 2
Article Number: ARTN 100346
Journal Issue: 2
DOI: 10.1016/j.xcrp.2020.100346
Abstract
Over the past decade, perovskite solar cells have become one of the major research interests of the photovoltaic community, and they are now on the brink of catching up with the classical inorganic solar cells, with efficiency now reaching up to 25%. However, significant improvements are still achievable by reducing recombination losses. The aim of this work is to develop a fast and easy-to-use tool to pinpoint the main losses in perovskite solar cells. We use large-scale drift-diffusion simulations to get a better understanding of the light intensity dependence of the open-circuit voltage and how it correlates to the dominant recombination process. We introduce an automated identification tool using machine learning methods to pinpoint the dominant loss using the light intensity-dependent performances as an input. The machine learning was trained using >2 million simulations and gives an accuracy of the prediction up to 82%.
Authors with CRIS profile
Involved external institutions
Netherlands (NL)How to cite
APA:
Le Corre, V.M., Sherkar, T.S., Koopmans, M., & Koster, L.J.A. (2021). Identification of the dominant recombination process for perovskite solar cells based on machine learning. Cell Reports Physical Science, 2(2). https://doi.org/10.1016/j.xcrp.2020.100346
MLA:
Le Corre, Vincent Marc, et al. "Identification of the dominant recombination process for perovskite solar cells based on machine learning." Cell Reports Physical Science 2.2 (2021).
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