Machine Learning-Assisted Prediction of Ambient-Processed Perovskite Solar Cells’ Performances

Dowon Pyun, Seungtae Lee, Solhee Lee, Seok-Hyun Jeong, Jae-Keun Hwang, Kyunghwan Kim, Youngmin Kim, Jiyeon Nam, Sujin Cho, Ji-Seong Hwang, Wonkyu Lee, Sangwon Lee, Hae-Seok Lee, Donghwan Kim and Yoonmook Kang ()
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Dowon Pyun: Department of Materials Science and Engineering, Korea University, Seoul 02841, Republic of Korea
Seungtae Lee: Department of Materials Science and Engineering, Korea University, Seoul 02841, Republic of Korea
Solhee Lee: Department of Materials Science and Engineering, Korea University, Seoul 02841, Republic of Korea
Seok-Hyun Jeong: Department of Materials Science and Engineering, Korea University, Seoul 02841, Republic of Korea
Jae-Keun Hwang: Department of Materials Science and Engineering, Korea University, Seoul 02841, Republic of Korea
Kyunghwan Kim: Department of Materials Science and Engineering, Korea University, Seoul 02841, Republic of Korea
Youngmin Kim: Graduate School of Energy and Environment (KU-KIST Green School), Korea University, Seoul 02841, Republic of Korea
Jiyeon Nam: Department of Materials Science and Engineering, Korea University, Seoul 02841, Republic of Korea
Sujin Cho: Department of Materials Science and Engineering, Korea University, Seoul 02841, Republic of Korea
Ji-Seong Hwang: Department of Materials Science and Engineering, Korea University, Seoul 02841, Republic of Korea
Wonkyu Lee: Department of Materials Science and Engineering, Korea University, Seoul 02841, Republic of Korea
Sangwon Lee: Graduate School of Energy and Environment (KU-KIST Green School), Korea University, Seoul 02841, Republic of Korea
Hae-Seok Lee: Graduate School of Energy and Environment (KU-KIST Green School), Korea University, Seoul 02841, Republic of Korea
Donghwan Kim: Department of Materials Science and Engineering, Korea University, Seoul 02841, Republic of Korea
Yoonmook Kang: Graduate School of Energy and Environment (KU-KIST Green School), Korea University, Seoul 02841, Republic of Korea

Energies, 2024, vol. 17, issue 23, 1-12

Abstract: As we move towards the commercialization and upscaling of perovskite solar cells, it is essential to fabricate them in ambient environment rather than in the conventional glove box environment. The efficiency of ambient-processed perovskite solar cells lags behind those fabricated in controlled environments, primarily owing to external environmental factors such as humidity and temperature. In the case of device fabrication in ambient environments, relying solely on a single parameter, such as temperature or humidity, is insufficient for accurately characterizing environmental conditions. Therefore, the dew point is introduced as a parameter which accounts for both temperature and humidity. In this study, a machine learning model was developed to predict the efficiency of ambient-processed perovskite solar cells based on meteorological data, particularly the dew point. A total of 238 perovskite solar cells were fabricated, and their photovoltaic parameters and dew points were collected from March to December 2023. The collected data were used to train various tree-based machine learning models, with the random forest model achieving the highest accuracy. The efficiencies of the perovskite solar cells fabricated in January and February 2024 were predicted with a MAPE of 4.44%. An additional Shapley Additive exPlanations analysis confirmed the significance of the dew point in the performance of perovskite solar cells.

Keywords: ambient processed; perovskite solar cells; dew point; artificial intelligence; machine learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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