Disentangling the Effects of Simultaneous Environmental Variables on Perovskite Synthesis and Device Performance via Interpretable Machine Learning
Authors:
Tianran Liu,
Nicky Evans,
Kangyu Ji,
Ronaldo Lee,
Aaron Zhu,
Vinn Nguyen,
James Serdy,
Elizabeth Wall,
Yongli Lu,
Florian A. Formica,
Moungi G. Bawendi,
Quinn C. Burlingame,
Yueh-Lin Loo,
Vladimir Bulovic,
Tonio Buonassisi
Abstract:
Despite the rapid rise in perovskite solar cell efficiency, poor reproducibility remains a major barrier to commercialization. Film crystallization and device performance are highly sensitive to environmental factors during fabrication, yet their complex interactions are not well understood. In this work, we present a systematic framework to investigate the influence of both individual and coupled…
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Despite the rapid rise in perovskite solar cell efficiency, poor reproducibility remains a major barrier to commercialization. Film crystallization and device performance are highly sensitive to environmental factors during fabrication, yet their complex interactions are not well understood. In this work, we present a systematic framework to investigate the influence of both individual and coupled environmental variables on device efficiency and crystallization kinetics. We developed an integrated fabrication platform with precise, independent control over ambient solvent partial pressure, absolute humidity, and temperature during spin-coating and thermal-annealing processes, respectively. Using the platform, we implemented a closed-loop Bayesian optimization framework to efficiently explore the multi-dimensional processing space. We mapped the impact of these environmental variables on device performance and identified coupled effects among them. In-situ grazing-incidence wide-angle X-ray scattering measurements further validated these findings by revealing a nonlinear interaction between absolute humidity and solvent partial pressure during spin-coating, which affects crystallization dynamics. To isolate and quantify these interactions, we developed an interpretable machine learning approach that combines knowledge distillation with Shapley interaction analysis. The model revealed the contribution of each interaction varies across different processing conditions. Our study highlights the importance of integrated ambient sensing and control to achieve repeatable perovskite solar cells, and demonstrates the utility of combining active learning with interpretable machine learning to navigate complex, high-dimensional processing landscapes.
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Submitted 27 August, 2025;
originally announced September 2025.