Towards a fully differentiable digital twin for solar cells

Towards a fully differentiable digital twin for solar cells Marie Louise Schubert1,2,†, Houssam Metni1,2,†, Jan David Fischbach1, Benedikt Zerulla1, Marjan Krstić14, Ulrich W. Paetzold3,4, Seyedamir Orooji3,4, Olivier J. J. Ronsin7, Yasin Ameslon7,8, Jens Harting7,8,9, Thomas Kirchartz10,11, Sandheep Ravishankar10, Chris Dreessen10, Eunchi Kim10, Christian Sprau4,5, Mohamed Hussein4,6, Alexander Colsmann4,5, Karen Forberich7, Klaus Jäger12,13, Pascal Friederich1,2,⋆, and Carsten Rockstuhl1,14,⋆ 1Institute of Nanotechnology, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany 2Institute of Theoretical Informatics, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany 3Institute of Microstructure Technology, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany 4Light Technology Institute, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany 5Material Research Center for Energy Systems, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany 6Department of Physics, Faculty of Science, Ain Shams University, Cairo, Egypt 7Helmholtz-Institute Erlangen-Nürnberg for Renewable Energy (IET-2), Forschungszentrum Jülich, Erlangen, Germany 8Department of Chemical and Biological Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany 9Department of Physics, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany 10IMD-3 Photovoltaik, Forschungszentrum Jülich (FZJ), Jülich, Germany 11Faculty of Electrical Engineering and Information Technology, University of Duisburg-Essen, Duisburg, Germany 12Department Optics for Solar Energy (SE-AOPT), Helmholtz-Zentrum Berlin für Materialien und Energie GmbH, Berlin, Germany 13Zuse Institute Berlin, Berlin, Germany 14Institute of Theoretical Solid State Physics, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany †These authors contributed equally; marie.schubert@kit.edu and houssam.metni@kit.edu ⋆These authors are corresponding authors; pascal.friederich@kit.edu and carsten.rockstuhl@kit.edu 1 arXiv:2512.02904v1 [physics.comp-ph] 2 Dec 2025 Abstract Maximizing energy yield (EY) – the total electric energy generated by a solar cell within a year at a specific location – is crucial in photovoltaics (PV), especially for emerging technolo- gies. Computational methods provide the necessary insights and guidance for future research. However, existing simulations typically focus on only isolated aspects of solar cells. This lack of consistency highlights the need for a framework unifying all computational levels, from ma- terial to cell properties, for accurate prediction and optimization of EY prediction. To address this challenge, a differentiable digital twin, Sol(Di)2T, is introduced to enable comprehen- sive end-to-end optimization of solar cells. The workflow starts with material properties and morphological processing parameters, followed by optical and electrical simulations. Finally, climatic conditions and geographic location are incorporated to predict the EY. Each step is either intrinsically differentiable or replaced with a machine-learned surrogate model, enabling not only accurate EY prediction but also gradient-based optimization with respect to input parameters. Consequently, Sol(Di)2T extends EY predictions to previously unexplored con- ditions. Demonstrated for an organic solar cell, the proposed framework marks a significant step towards tailoring solar cells for specific applications while ensuring maximal performance. 1 Introduction Modern photovoltaic applications for building integration or in agricultural settings require solar cell technologies with customizable properties. Emerging PV technologies offer a promising solution to this need [1]. Among the many research directions in the context of emerging PV technologies, organic (OPV) and perovskite solar cells stand out [2]. These PV technologies feature mechanical flexibility, lightweight design and have the potential for cost-effective production [3, 4, 5, 6]. The cells can be made transparent and in tunable colors [7], expanding their applicability in design- sensitive contexts. Furthermore, some of these technologies have demonstrated competitive power conversion efficiencies (PCE) [8, 9, 10]. In recent years, the energy yield (EY) has become an important technological objective in the PV research community. The EY encompasses the total electric energy generated by solar cells within a year in a given location, accounting for external factors such as irradiance and temperature [11]. Computational tools have been developed to better understand PV processes and to predict the EY for specific solar cell settings [12, 13, 14, 15, 16]. Despite this progress, many existing computational tools are developed independently and only focus on isolated aspects of solar cells. Therefore, a comprehensive material-to-application EY simulation framework that targets specific materials, processes, and device characteristics is highly desirab

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