Publication date: 22nd July 2026
The enthusiasm of the scientific community towards identifying alternative materials for photovoltaics has led to the quick development of halide perovskites. Yet, they lack stability upon exposure to outdoor, environmental conditions. Because this burgeoning class of material entails a colossal chemical composition space, machine learning (ML) is very suitable to replace the conventional trial-and-error approach used in their characterization [1,2]. I will discuss how we have implemented ML algorithms varying from echo state networks to statistical models to classify and predict physical properties such as hole transport layer electrical conductivity and halide perovskite photoluminescence response [3,4]. Through automated, in situ optical measurements, we were able to predict the response of these materials for >50 hours, with >90% accuracy [5]. Moreover, we established hidden correlations between composition and thermal stability [6]. Our high-throughput measurements and ML-supported analyses validate the potential of ML to forecast perovskites’ response with a variety of chemical compositions [7,8].
References:
[1] J. M. Howard, E. M. Tennyson, B. R. A. Neves, M. S. Leite. Machine Learning for Perovskites’ Reap-Rest-Recovery Cycle. Joule 3, 325 (2019)
[2] M. Srivastava, J. M. Howard, T. Gong, M. R. S. Dias, and M. S. Leite. Machine Learning Roadmap for Perovskite Photovoltaics. J. Phys. Chem. Lett. 12, 7866 (2021).
[3] J. M. Howard, K. Palm, Q. Wang, E. Lee, A. Abate, J. N. Munday, and M. S. Leite. Water-Induced and Wavelength-Dependent Light Absorption and Emission Dynamics in Triple-Cation Halide Perovskites. Advanced Optical Materials 9, 2100710 (2021).
[4] J. M. Howard, Q. Wang, M. Srivastava, T. Gong, E. Lee, A. Abate, and M. S. Leite. Quantitative Predictions of Moisture-Driven Photo-Emission Dynamics in Metal Halide Perovskites via Machine Learning. J. Phys. Chem. Letters 13, 2254 (2022).
[5] M. Srivastava, A. R. Hering, Y. An, J.-P. Correa-Baena, and M. S. Leite. Machine Learning Enables Prediction of Halide Perovskites’ Optical Behavior with >90% Accuracy. ACS Energy Letters 8, 1716 (2023).
[6] A. R. Hering, M. Dubey, E. Hosseini, M. Srivastava, Y. An, J.-P. Correa-Baena, H. Homayoun, and M. S. Leite. Machine Learning Reveals Composition Dependent Thermal Stability in Halide Perovskites. In review (2025).
[7] A. R, Hering, M. Dubey, and M. S. Leite. Emerging Opportunities for Hybrid Perovskite Solar Cells using Machine Learning. APL Energy 1, 020901 (2023).
[8] A. R, Hering, C. Sutter-Fella, and M. S. Leite. An AI-Accelerated Pathway for Stable and Reproducible Halide Perovskites. Submitted (2025).
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