Machine learning-assisted antisolvent screening for highly efficient DMSO-free tin halide perovskite solar cells

Machine learning-assisted antisolvent screening for highly efficient DMSO-free tin halide perovskite solar cellsMahmoud Hussein a, Titan Hartono a, Ece Aktas b, Antonio Abate aa Helmholtz-Zentrum Berlin für Materialien und Energie, 14109, Berlin, Germanyb Department of Chemical, Materials and Production Engineering, University of Naples Federico II, Piazzale Tecchio 80, 80125, Fuorigrotta, ItalyMaterials for Sustainable Development Conference (MATSUS)Proceedings of MATSUS Spring 2024 Conference (MATSUS24)#PeroFF - Perovskite: from fundamentals to device fabricationBarcelona, Spain, 2024 March 4th - 8thOrganizers: Lioz Etgar, Wang Feng and Michael SalibaOral, Mahmoud Hussein, presentation 218DOI: https://doi.org/10.29363/nanoge.matsus.2024.218Publication date: 18th December 2023Perovskite solar cells (PSCs) are the most promising PV technology in recent years. Efficiency rocketed from 3.1% to 26% in the last decade. The highest-performing PSCs are lead-based, which increases concerns about the environmental impact of this type of solar cell. Consequently, interest is growing in tin-based PSCs as an environmentally friendly alternative to the lead counterpart. However, tin perovskites are hindered by the tin (II) oxidation to tin (IV), which leads to self-doping and devastating cell performance. DMSO, the universal solvent for tin perovskites, was found to oxidize tin [1]. In our previous work, we introduced a group of 15 different solvents that can form 1 Molar solution of FASnI3 [2]. However, the film formation dynamics were challenging due to unsuitable coordination between the solvent molecules and the metal, leading to bad micro-structured films. In this work, we introduce a high-throughput screening of 73 antisolvents against the previously introduced solvents to engineer the crystallization dynamics in tin perovskites. Then, we feed the resultant data into a machine learning algorithm with the solvents and the antisolvents parameters to conclude the most related parameters that control the solvent-antisolvent interaction in tin halide perovskites. In addition, the algorithm predicts the most effective solvent-antisolvent pairs that can form a perovskite film based on the film darkness prediction. Furthermore, we use Hansen parameters spheres to explain the relationship between the solvents and the antisolvents that make the highest-performing perovskite film. Finally, we test the most promising tin perovskite films for their efficiency and report a PCE of around 9%, the highest DMSO-free tin halide PSC as far as we know. References:[1] Jorge Pascual, Giuseppe Nasti, M. H. Aldamasy, J. A. Smith, Marion Flatken, Nga Phung, D. D. Girolamo, Silver-Hamill Turren-Cruz, Meng Li, André Dallmann, Roberto Avolio and Antonio Abate[2] D. Di Girolamo, J. Pascual, M. H. Aldamasy, Z. Iqbal, G. Li, E. Radicchi, M. Li, S.-H. Turren-Cruz, G. Nasti, A. Dallmann, F. De Angelis and A. Abate© 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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