Authors: Felix Laufer | Lukas Klein | Sebastian Ziegler | Charlotte Debus | Markus Götz | Klaus Maier‐Hein | Ulrich W. Paetzold | Fabian Isensee | Paul F. Jäger
Source:  (article, 2023-12-18)
Field: Materials Science — Materials Chemistry

Analyzing Perovskite Thin-Film Formation with Machine Learning and Explainable AI Analyzing Perovskite Thin-Film Formation with Machine Learning and Explainable AIFelix Laufer a, Lukas Klein b, c, d, Sebastian Ziegler e, d, Charlotte Debus f, g, Markus Götz f, g, Klaus Maier-Hein e, d, Ulrich W. Paetzold a, h, Fabian Isensee e, d, Paul F. Jäger b, da Light Technology Institute, Karlsruhe Institute of Technology, Engesserstr. 13, Karlsruhe, 76131, Germanyb Interactive Machine Learning Group, German Cancer Research Centerc Institute for Machine Learning, ETH Zürichd Helmholtz Imaging, German Cancer Research Centere Division of Medical Image Computing, German Cancer Research Centerf Steinbuch Centre for Computing, Karlsruhe Institute of Technologyg Helmholtz AI, Karlsruhe Institute of Technologyh Institute of Microstructure Technology, Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, GermanyMaterials for Sustainable Development Conference (MATSUS)Proceedings of MATSUS Spring 2024 Conference (MATSUS24)#AI - Automation and Nanomaterials (machine learning, artificial intelligence, robotics, accelerated discovery)Barcelona, Spain, 2024 March 4th - 8thOrganizers: Ivan Infante and Oleksandr VoznyyOral, Felix Laufer, presentation 215DOI: https://doi.org/10.29363/nanoge.matsus.2024.215Publication date: 18th December 2023Perovskite solar cells (PSCs) hold great potential as a technology for the next generation of thin-film photovoltaics. For PSCs to be commercially viable, scalable fabrication processes must be improved based on state-of-the-art techniques used for small-area devices. Hence, there is a need to investigate and optimize the formation of perovskite thin films fabricated through scalable deposition methods to facilitate large-scale production of solution-based, large-area PSCs. However, the formation of perovskite thin films from precursor solution is complex and involves the interrelated phases of drying, nucleation, and crystal growth. Therefore, in-depth understanding and precise control of these stages are vital to consistently fabricate high-quality optoelectronic films. The application of in situ multi-channel photoluminescence (PL) imaging allows recording the temporal evolution of the perovskite thin-film formation, while also providing spatial resolution and spectral information. We have generated and made publicly available unique experimental data that comprises in situ PL data of blade-coated PSCs and corresponding quality measures, such as power conversion efficiency and perovskite layer thickness. Exploring this in situ PL data has the potential to reveal important variations in the thin-film formation process, however, the limits of human analysis are exceeded due to the high dimensionality. Here, machine learning (ML) methods are a promising route to investigate such complex, multi-dimensional PL data to elucidate the large-area formation of blade-coated perovskite thin-films during vacuum quenching. To accelerate empirical-based traditional, incremental scientific progress, we employ deep learning and explainable artificial intelligence (XAI) techniques to identify correlations between the in situ PL data collected during the perovskite thin-film fabrication and resulting solar cell performance metrics, while making these correlations humanly understandable. Our analysis shows that variations in the quality of PSCs can be understood by examining the thin-film formation process with ML and XAI. By applying various XAI methods we explain not only which data features are important but also provide a data-driven explanation why they are important. Additionally, our research illustrates how these insights can be translated into practical guidelines for optimizing perovskite thin-film fabrication by giving actionable recommendations to experimental scientists. Remarkably, we are able to generate these insights just by analyzing the given dataset without having to perform additional laborious and expensive trial-and-error experiments. Our research improves the understanding of the complex large-area formation of perovskite thin-films and highlights the pivotal role of ML, and XAI methodologies in particular, in accelerating energy materials research. As XAI methods are not limited to the investigated dataset, this study is a prime example of how similar analyses can be performed to interpret and improve processes in numerous other areas of sustainable materials research. For an in-depth analysis and further discussion of these findings, see our full research paper "Discovering Process Dynamics for Scalable Perovskite Solar Cell Manufacturing with Explainable AI", L. Klein*, S. Ziegler*, F. Laufer et al., Adv. Mater., 202307160 (2023), doi: 10.1002/adma.202307160. © FUNDACIO DE LA COMUNITAT VALENCIANA SCITOnanoGe is a prestigious brand of successful science conferences that are developed along the year in different areas of the world since 2009. 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The conference also covers the developments of perovskite optoelectronics, including light-emitting diodes, lasers, optical devices, nanophotonics, nonlinear optical properties, colloidal nanostructures, photophysics and light-matter coupling.International Conference on Perovskite Thin Film Photovoltaics Perovskite Photonics and OptoelectronicsThe International Conference on Perovskite Thin Film Photovoltaics Perovskite Photonics and Optoelectronics (NIPHO) is the best place to hear the latest developments in perovskite solar cells as well as on recent advances in the fields of perovskite light-emitting diodes, lasers, optical devices, nanophotonics, nonlinear optical properties, colloidal nanostructures, photophysics and light-matter coupling. Perovskite (structure) ; Computer science ; Materials science ; Thin film ; Artificial intelligence ; Chemical engineering ; Nanotechnology ; Engineering Machine Learning in Materials Science