A digital twin to overcome long-time challenges in photovoltaics
Lüer L, Peters IM, Smith AS, Dorschky E, Eskofier B, Liers-Bergmann F, Franke J, Sjarov M, Brossog M, Guldi DM, Maier A, Brabec C (2024)
Publication Language: English
Publication Type: Journal article
Publication year: 2024
Journal
Book Volume: 8
Pages Range: 1-17
DOI: 10.1016/j.joule.2023.12.010
Abstract
The recent successes of emerging photovoltaics (PVs) are largely driven by innovations in material science. However, closing the gap to commercialization still requires significant progress to match contradicting requirements, such as performance, longevity, and recyclability. In this perspective, we envision the layout of a digital twin for PV materials able to provide the necessary acceleration of innovation. The layout combines machine learning approaches, as performed in materials acceleration platforms (MAPs), with physical models and digital twin concepts used in engineering. This layout will allow the use of high-throughput (HT) experimentation in MAPs to improve the parametrization of quantum chemical and solid-state models. In turn, the improved and generalized models can be used to obtain the crucial structural parameters. HT experimentation will thus yield a detailed understanding of generally valid structure-property relationships. We discuss the current state of the enabling technologies, namely fast scale-bridging surrogate models and large-scale optimization under uncertainty, and identify promising approaches for further development.
Authors with CRIS profile
Larry Lüer
Institute Materials for Electronics and Energy Technology (i-MEET)
Ana-Suncana Smith
Physics Underlying Life Science
Eva Dorschky
Machine Learning and Data Analytics Lab
Björn Eskofier
Machine Learning and Data Analytics Lab
Frauke Liers-Bergmann
Professur für Optimization under Uncertainty & Data Analysis
Jörg Franke
Lehrstuhl für Fertigungsautomatisierung und Produktionssystematik
Martin Sjarov
Lehrstuhl für Fertigungsautomatisierung und Produktionssystematik
Matthias Brossog
Lehrstuhl für Fertigungsautomatisierung und Produktionssystematik
Dirk Michael Guldi
Lehrstuhl für Physikalische Chemie I
Andreas Maier
Lehrstuhl für Informatik 5 (Mustererkennung)
Christoph Brabec
Institute Materials for Electronics and Energy Technology (i-MEET)
Additional Organisation(s)
Involved external institutions
Germany (DE)How to cite
APA:
Lüer, L., Peters, I.M., Smith, A.-S., Dorschky, E., Eskofier, B., Liers-Bergmann, F.,... Brabec, C. (2024). A digital twin to overcome long-time challenges in photovoltaics. Joule, 8, 1-17. https://doi.org/10.1016/j.joule.2023.12.010
MLA:
Lüer, Larry, et al. "A digital twin to overcome long-time challenges in photovoltaics." Joule 8 (2024): 1-17.
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