Machine learning enhanced characterization and optimization of photonic cured MAPbI3 for efficient perovskite solar cells

Allen, Cody R.; Bhandari, Bishal; Xu, Weijie; Lee, Mark; Hsu, Julia P.

doi:10.20517/jmi.2024.72

Machine learning enhanced characterization and optimization of photonic cured MAPbI₃ for efficient perovskite solar cells

Journal Article · Mon Dec 30 00:00:00 EST 2024 · Journal of Materials Informatics (Online)

DOI:https://doi.org/10.20517/jmi.2024.72· OSTI ID:2544433

Allen, Cody R. ^[1]; Bhandari, Bishal; Xu, Weijie; Lee, Mark;

University of Texas at DAllas

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 (MAPbI₃) films by quantitatively comparing their 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 MAPbI3 film with an 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 MAPbI₃ and the hole transport layer (HTL) during PC critically degrades the PSC performance. By adding a buffer layer between the HTL and MAPbI₃, the optimized PC PSCs produce a champion PCE of 11.8%, comparable to the TA reference of 11.7%. 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 MAPbI₃ films.

View Accepted Manuscript (DOE)

Research Organization:: University of Texas at Dallas

Sponsoring Organization:: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office

Grant/Contract Number:: EE0009518

OSTI ID:: 2544433

Journal Information:: Journal of Materials Informatics (Online), Journal Name: Journal of Materials Informatics (Online) Vol. 4; ISSN 2770-372X

Publisher:: OAE PublishingCopyright Statement

Country of Publication:: United States

Language:: English

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Machine learning enhanced characterization and optimization of photonic cured MAPbI3 for efficient perovskite solar cells

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Machine learning enhanced characterization and optimization of photonic cured MAPbI₃ for efficient perovskite solar cells