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Journal of applied research and technology

On-line version ISSN 2448-6736Print version ISSN 1665-6423

Abstract

VELEZ, J. et al. Absorber layer thickness as a new feature in statistical learning tools of Perovskite solar cells. J. appl. res. technol [online]. 2023, vol.21, n.5, pp.858-865.  Epub Aug 23, 2024. ISSN 2448-6736.  https://doi.org/10.22201/icat.24486736e.2023.21.5.2057.

Recently, the development of Perovskite-based solar cells has emerged as a technological alternative to photovoltaic generation with a higher efficiency/cost ratio. Many contributions have been made in recent years, as evidenced by many academic publications with worldwide experimental results in this area. Machine learning as a tool can support the development of this technology by predicting new materials and discovering novel solar cell configurations. However, the implementation of these methods implies the selection of suitable descriptors. In the present work, we analyze the statistical relationship between the thickness of the absorber layer and solar cell performance parameters. We evaluated the use of the absorber layer thickness as a descriptor in a linear regression model using a database of 221 literature records containing information on the bandgap, the ∆HOMO (Perovskite-HTL), and ∆LUMO (Perovskite-ETL) of different Perovskite cells, together with the thickness of the absorber layer. By building two multiple linear regression models, including or not the thickness of the absorber layer, a reduction in the root means square error of 4.4% and 2.8% was found in the prediction of the Jsc and PCE, respectively. By applying a linear regression model, an improvement in the prediction of Jsc can be seen due to the inclusion of thickness as a descriptor, which is in line with the high value of the mutual information measure we found between the thickness and Jsc.

Keywords : Perovskite solar cell; Machine learning; Thickness; Mutual information.

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