Unraveling the Effect of Compositional Ratios on the Kesterite Thin-Film Solar Cells Using Machine Learning Techniques

Unraveling the Effect of Compositional Ratios on the Kesterite Thin-Film Solar Cells Using Machine Learning Techniques

In the Kesterite family, the Cu<sub>2</sub>ZnSn(S,Se)<sub>4</sub> (CZTSSe) thin-film solar cells (TFSCs) have demonstrated the highest device efficiency with non-stoichiometric cation composition ratios. These composition ratios have a strong influence on the structural, opti...

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Bibliographic Details
Main Authors: Vijay C. Karade, Santosh S. Sutar, Jun Sung Jang, Kuldeep Singh Gour, Seung Wook Shin, Mahesh P. Suryawanshi, Rajanish K. Kamat, Tukaram D. Dongale, Jin Hyeok Kim, Jae Ho Yun
Format: Article
Language:English
Published: MDPI AG 2023-11-01
Series:Crystals
Subjects:
CZTSSe
thin-film solar cells
machine learning
compositional ratio
prediction
Online Access:https://www.mdpi.com/2073-4352/13/11/1581

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