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9 March 2024 Accelerating the understanding of halide perovskites through machine learning
Marina S. Leite
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Proceedings Volume PC12881, Physics, Simulation, and Photonic Engineering of Photovoltaic Devices XIII; PC128810G (2024) https://doi.org/10.1117/12.3000120
Event: SPIE OPTO, 2024, San Francisco, California, United States
Abstract
Machine learning (ML) is a powerful tool to accelerate the development of halide perovskite materials and devices. We apply ML models varying from echo state networks to statistical models to classify and predict physical properties such as hole transport layer electrical conductivity, halide perovskite photoluminescence response, the power conversion efficiency of photovoltaic devices, etc. Specifically, we use in situ environmental optical measurements to predict the optical behavior of Cs-FA perovskites for 50+ hours, upon materials’ exposure to moisture.
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Marina S. Leite "Accelerating the understanding of halide perovskites through machine learning", Proc. SPIE PC12881, Physics, Simulation, and Photonic Engineering of Photovoltaic Devices XIII, PC128810G (9 March 2024); https://doi.org/10.1117/12.3000120
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KEYWORDS
Perovskite
Machine learning
Chemical composition
Photovoltaics
Linear regression
Moisture
Optical testing
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