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Allen et al. J Mater Inf 2024;4:35 Journal of
DOI: 10.20517/jmi.2024.72
Materials Informatics

Research Article Open Access

Machine learning enhanced characterization and
optimization of photonic cured MAPbI for efficient
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perovskite solar cells

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Cody R. Allen , Bishal Bhandari , Weijie Xu , Mark Lee , Julia W. P. Hsu 2,*
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Department of Physics, The University of Texas at Dallas, Richardson, TX 75080, USA.
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Department of Material Science and Engineering, The University of Texas at Dallas, Richardson, TX 75080, USA.
* Correspondence to: Dr. Julia W. P. Hsu, Department of Material Science and Engineering, The University of Texas at Dallas,
800 W Campbell Road, Richardson, TX 75080, USA. E-mail: jwhsu@utdallas.edu
How to cite this article: Allen CR, Bhandari B, Xu W, Lee M, Hsu JWP. Machine learning enhanced characterization and
optimization of photonic cured MAPbI for efficient perovskite solar cells. J Mater Inf 2024;4:35. https://dx.doi.org/10.20517/
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jmi.2024.72
Received: 12 Nov 2024 First Decision: 3 Dec 2024 Revised: 11 Dec 2024 Accepted: 17 Dec 2024 Published: 31 Dec 2024

Academic Editors: Ming Hu, Baisheng Sa Copy Editor: Pei-Yun Wang Production Editor: Pei-Yun Wang

Abstract
Photonic curing (PC) can facilitate high-speed perovskite solar cell (PSC) manufacturing because it uses
high-intensity light pulses to crystallize perovskite films in milliseconds. However, optimizing PC conditions is
challenging due to its many variables, and using power conversion efficiency (PCE) as the optimization metric is
both time-consuming and labor-intensive. This work presents a machine learning (ML) approach to optimize PC
conditions for fabricating methylammonium lead iodide (MAPbI ) films by quantitatively comparing their
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ultraviolet-visible (UV-vis) absorbance spectra to thermal annealed (TA) films using four similarity metrics. We
perform Bayesian optimization coupled with Gaussian process regression (BO-GP) to minimize the similarity
metrics. Refining PC conditions using active learning based on BO-GP models, we achieve a PC MAPbI film with an
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absorbance spectrum closely matching a TA reference film, which is further verified by its crystalline and
morphological properties. Thus, we demonstrate that the UV-vis absorption spectrum can accurately proxy film
quality. Additionally, we use an AI-based segmentation model for a more efficient grain size analysis. However,
when we use the optimized PC condition to fabricate PSCs, we find that interaction between MAPbI and the hole
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transport layer (HTL) during PC critically degrades the PSC performance. By adding a buffer layer between the HTL
and MAPbI , the optimized PC PSCs produce a champion PCE of 11.8%, comparable to the TA reference of 11.7%.
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Using UV-vis similarity metrics instead of device PCE as the objective in our BO-GP method accelerates the
optimization of PC processing conditions for MAPbI films.
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Keywords: Perovskite solar cells, Bayesian optimization, photonic curing, image segmentation, machine learning

© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0
International License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, sharing,
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long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and
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