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https://doi.org/10.21256/zhaw-29905| Publikationstyp: | Beitrag in wissenschaftlicher Zeitschrift |
| Art der Begutachtung: | Peer review (Publikation) |
| Titel: | Identifying performance limiting parameters in perovskite solar cells using machine learning |
| Autor/-in: | Zbinden, Oliver Knapp, Evelyne Tress, Wolfgang |
| et. al: | No |
| DOI: | 10.1002/solr.202300999 10.21256/zhaw-29905 |
| Erschienen in: | Solar RRL |
| Band(Heft): | 8 |
| Heft: | 6 |
| Seite(n): | 2300999 |
| Erscheinungsdatum: | 2024 |
| Verlag / Hrsg. Institution: | Wiley |
| ISSN: | 2367-198X |
| Sprache: | Englisch |
| Schlagwörter: | Machine learning; Optimization; Perovskite solar cell |
| Fachgebiet (DDC): | 006: Spezielle Computerverfahren 621.3: Elektro-, Kommunikations-, Steuerungs- und Regelungstechnik |
| Zusammenfassung: | Herein, it is shown that machine learning (ML) methods can be used to predict the parameter that limits the solar-cell performance most significantly, solely based on the current density–voltage (J–V) curve under illumination. The data (11’150 J–V curves) to train the model is based on device simulation, where 20 different physical parameters related to charge transport and recombination are varied individually. This approach allows to cover a wide range of effects that could occur when varying fabrication conditions or during degradation of a device. Using ML, the simulated J–V curves are classified for the changed parameter with accuracies above 80%, where Random Forests perform best. It turns out that the key parameters, short-circuit current density, open-circuit voltage, maximum power conversion efficiency, and fill factor are sufficient for accurate predictions. To show the practical relevance, the ML algorithms are then applied to reported devices, and the results are discussed from a physics perspective. It is demonstrated that if some specified conditions are met, satisfying results can be reached. The proposed workflow can be used to better understand a device's behavior, e.g., during degradation, or as a guideline to improve its performance without costly and time-consuming lab-based trial-and-error methods. |
| URI: | https://digitalcollection.zhaw.ch/handle/11475/29905 |
| Volltext Version: | Eingereichte Version |
| Lizenz (gemäss Verlagsvertrag): | Lizenz gemäss Verlagsvertrag |
| Gesperrt bis: | 2025-01-31 |
| Departement: | School of Engineering |
| Organisationseinheit: | Institute of Computational Physics (ICP) |
| Enthalten in den Sammlungen: | Publikationen School of Engineering |
Dateien zu dieser Ressource:
| Datei | Beschreibung | Größe | Format | |
|---|---|---|---|---|
| 2024_Zbinden-etal_Performance-limiting-parameters-in-PSCs_submitted.pdf | Submitted Version | 6.86 MB | Adobe PDF |  Öffnen/Anzeigen |
| 2024_Zbinden-etal_Performance-limiting-parameters-in-PSCs_accepted.pdf Bis 2025-01-31 | Accepted Version | 14.48 MB | Adobe PDF | Öffnen/Anzeigen |
Zur Langanzeige
Zbinden, O., Knapp, E., & Tress, W. (2024). Identifying performance limiting parameters in perovskite solar cells using machine learning. Solar RRL, 8(6), 2300999. https://doi.org/10.1002/solr.202300999
Zbinden, O., Knapp, E. and Tress, W. (2024) ‘Identifying performance limiting parameters in perovskite solar cells using machine learning’, Solar RRL, 8(6), p. 2300999. Available at: https://doi.org/10.1002/solr.202300999.
O. Zbinden, E. Knapp, and W. Tress, “Identifying performance limiting parameters in perovskite solar cells using machine learning,” Solar RRL, vol. 8, no. 6, p. 2300999, 2024, doi: 10.1002/solr.202300999.
ZBINDEN, Oliver, Evelyne KNAPP und Wolfgang TRESS, 2024. Identifying performance limiting parameters in perovskite solar cells using machine learning. Solar RRL. 2024. Bd. 8, Nr. 6, S. 2300999. DOI 10.1002/solr.202300999
Zbinden, Oliver, Evelyne Knapp, and Wolfgang Tress. 2024. “Identifying Performance Limiting Parameters in Perovskite Solar Cells Using Machine Learning.” Solar RRL 8 (6): 2300999. https://doi.org/10.1002/solr.202300999.
Zbinden, Oliver, et al. “Identifying Performance Limiting Parameters in Perovskite Solar Cells Using Machine Learning.” Solar RRL, vol. 8, no. 6, 2024, p. 2300999, https://doi.org/10.1002/solr.202300999.
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