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10 March 2023 Automated Optical Measurements and Machine Learning Analysis of Cs-FA Halide Perovskites (Conference Presentation)
Abigail Hering, Meghna Srivastava, Yu An, Juan-Pablo Correa-Baena, Marina S. Leite
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Proceedings Volume PC12416, Physics, Simulation, and Photonic Engineering of Photovoltaic Devices XII; PC124160C (2023) https://doi.org/10.1117/12.2647194
Event: SPIE OPTO, 2023, San Francisco, California, United States
Abstract
To mitigate perovskites’ degradation, there have been a pressing need to identify the effects of environmental stressors on material physical behavior and device performance. We implement high-throughput environmental photoluminescence (PL) to interrogate the response of Cs-FA perovskites with a range of chemical composition while exposed to temperature and relative humidity cycles. These measurements are used as input when comparing how machine learning methods can be realized to forecast material response. We quantitatively compare linear regression, Echo State Network (ESN), and Auto-Regressive Integrated Moving Average with eXogenous regressors (ARIMAX).
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Abigail Hering, Meghna Srivastava, Yu An, Juan-Pablo Correa-Baena, and Marina S. Leite "Automated Optical Measurements and Machine Learning Analysis of Cs-FA Halide Perovskites (Conference Presentation)", Proc. SPIE PC12416, Physics, Simulation, and Photonic Engineering of Photovoltaic Devices XII, PC124160C (10 March 2023); https://doi.org/10.1117/12.2647194
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KEYWORDS
Perovskite
Machine learning
Optical testing
Humidity
Luminescence
Photovoltaics
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