Quantitative Predictions of Moisture-Driven Photoemission Dynamics in Metal Halide Perovskites via Machine Learning
- John M. HowardJohn M. HowardDepartment of Materials Science and Engineering, University of Maryland, College Park, Maryland 20742, United StatesInstitute for Research in Electronics and Applied Physics, University of Maryland, College Park, Maryland 20742, United StatesMore by John M. Howard
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- Qiong WangQiong WangYoung Investigator Group Active Materials and Interfaces for Stable Perovskite Solar Cells, Helmholtz-Zentrum Berlin für Materialien und Energie, Kekuléstraße 5, 12489 Berlin, GermanyMore by Qiong Wang
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- Meghna SrivastavaMeghna SrivastavaDepartment of Materials Science and Engineering, University of California, Davis, Davis, California 95616, United StatesMore by Meghna Srivastava
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- Tao GongTao GongDepartment of Materials Science and Engineering, University of California, Davis, Davis, California 95616, United StatesDepartment of Electrical and Computer Engineering, University of California, Davis, Davis, California 95616, United StatesMore by Tao Gong
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- Erica LeeErica LeeDepartment of Materials Science and Engineering, University of Maryland, College Park, Maryland 20742, United StatesInstitute for Research in Electronics and Applied Physics, University of Maryland, College Park, Maryland 20742, United StatesMore by Erica Lee
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- Antonio AbateAntonio AbateYoung Investigator Group Active Materials and Interfaces for Stable Perovskite Solar Cells, Helmholtz-Zentrum Berlin für Materialien und Energie, Kekuléstraße 5, 12489 Berlin, GermanyDepartment of Chemical, Materials and Production Engineering, University of Naples Federico II, Piazzale Tecchio 80, 80125 Fuorigrotta, Naples, ItalyMore by Antonio Abate
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- Marina S. Leite*Marina S. Leite*Email [email protected]Department of Materials Science and Engineering, University of California, Davis, Davis, California 95616, United StatesMore by Marina S. Leite
Abstract
Metal halide perovskite (MHP) photovoltaics may become a viable alternative to standard Si-based technologies, but the current lack of long-term stability precludes their commercial adoption. Exposure to standard operational stressors (light, temperature, bias, oxygen, and water) often instigate optical and electronic dynamics, calling for a systematic investigation into MHP photophysical processes and the development of quantitative models for their prediction. We resolve the moisture-driven light emission dynamics for both methylammonium lead tribromide and triiodide thin films as a function of relative humidity (rH). With the humidity and photoluminescence time series, we train recurrent neural networks and establish their ability to quantitatively predict the path of future light emission with 18% error over 4 h. Together, our in situ rH-PL measurements and machine learning forecasting models provide a framework for the rational design of future stable perovskite devices and, thus, a faster transition toward commercial applications.
Cited By
This article is cited by 3 publications.
- Meghna Srivastava, Abigail R. Hering, Yu An, Juan-Pablo Correa-Baena, Marina S. Leite. Machine Learning Enables Prediction of Halide Perovskites’ Optical Behavior with >90% Accuracy. ACS Energy Letters 2023, 8 (4) , 1716-1722. https://doi.org/10.1021/acsenergylett.2c02555
- Kaining Zhang, Lang Chen, Kun Yang, Bin Zhang, Jianying Lu, Junying Wu. Prediction of Initial Reaction Characteristics of Materials from Molecular Conformational Changes Based on Artificial Intelligence Technology. The Journal of Physical Chemistry C 2022, 126 (50) , 21168-21180. https://doi.org/10.1021/acs.jpcc.2c02519
- Felix Laufer, Sebastian Ziegler, Fabian Schackmar, Edwin A. Moreno Viteri, Markus Götz, Charlotte Debus, Fabian Isensee, Ulrich W. Paetzold. Process Insights into Perovskite Thin‐Film Photovoltaics from Machine Learning with In Situ Luminescence Data. Solar RRL 2023, 4 , 2201114. https://doi.org/10.1002/solr.202201114