Regensburg 2022 – wissenschaftliches Programm
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O: Fachverband Oberflächenphysik
O 43: Frontiers of Electronic Structure Theory: Focus on Artificial Intelligence Applied to Real Materials 1
O 43.9: Vortrag
Mittwoch, 7. September 2022, 12:30–12:45, S054
Machine-learning Based Screening of Lead-free Perovskites for Photovoltaic Applications — •Elisabetta Landini1,3, Harald Oberhofer2, and Karsten Reuter1 — 1Fritz-Haber-Institut der Max-Planck-Gesellschaft, Faradayweg 4-6, D-14195 Berlin, Germany — 2Chair for Theoretical Physics VII, Physikalisches Institut Universität Bayreuth, 95440, Bayreuth, Germany — 3Chair for Theoretical Chemistry, Technische Universität München, Lichtenbergstr. 4, D-85747 Garching, Germany
Lead-free halide double perovskites are promising stable and non-toxic alternatives to methylammonium lead iodide in the field of photovoltaics. In this context, the most commonly used double perovskite is Cs2AgBiBr6, due to its favorable charge transport properties [1]. However, the maximum power conversion efficiency obtained for this material does not exceed 3%, as a consequence of its wide indirect gap and its intrinsic and extrinsic defects [2]. On the other hand, the materials space that arises from the substitution of different elements in the 4 lattice sites of this structure is large and still mostly unexplored.
In this work a neural network is used to predict the band gap of double perovskites from an initial space of 7056 structures and select candidates suitable for visible light absorption. Successive hybrid DFT calculations are used to evaluate the thermodynamic stability and the power conversion efficiency of the selected compounds, and propose novel potential solar absorbers.
[1] E.T. McClure et al., Chemistry of Materials 28, 1348 (2016).
[2] X. Yang et al., Energy & Fuels 34,10513 (2020).