Please use this identifier to cite or link to this item:
https://doi.org/10.21256/zhaw-29905Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Zbinden, Oliver | - |
| dc.contributor.author | Knapp, Evelyne | - |
| dc.contributor.author | Tress, Wolfgang | - |
| dc.date.accessioned | 2024-02-15T12:51:05Z | - |
| dc.date.available | 2024-02-15T12:51:05Z | - |
| dc.date.issued | 2024 | - |
| dc.identifier.issn | 2367-198X | de_CH |
| dc.identifier.uri | https://digitalcollection.zhaw.ch/handle/11475/29905 | - |
| dc.description.abstract | 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. | de_CH |
| dc.language.iso | en | de_CH |
| dc.publisher | Wiley | de_CH |
| dc.relation.ispartof | Solar RRL | de_CH |
| dc.rights | Licence according to publishing contract | de_CH |
| dc.subject | Machine learning | de_CH |
| dc.subject | Optimization | de_CH |
| dc.subject | Perovskite solar cell | de_CH |
| dc.subject.ddc | 006: Spezielle Computerverfahren | de_CH |
| dc.subject.ddc | 621.3: Elektro-, Kommunikations-, Steuerungs- und Regelungstechnik | de_CH |
| dc.title | Identifying performance limiting parameters in perovskite solar cells using machine learning | de_CH |
| dc.type | Beitrag in wissenschaftlicher Zeitschrift | de_CH |
| dcterms.type | Text | de_CH |
| zhaw.departement | School of Engineering | de_CH |
| zhaw.organisationalunit | Institute of Computational Physics (ICP) | de_CH |
| dc.identifier.doi | 10.1002/solr.202300999 | de_CH |
| dc.identifier.doi | 10.21256/zhaw-29905 | - |
| zhaw.funding.eu | info:eu-repo/grantAgreement/EC/H2020/851676//Defect Engineering, Advanced Modelling and Characterization for Next Generation Opto-Electronic-Ionic Devices/OptEIon | de_CH |
| zhaw.issue | 6 | de_CH |
| zhaw.originated.zhaw | Yes | de_CH |
| zhaw.pages.start | 2300999 | de_CH |
| zhaw.publication.status | submittedVersion | de_CH |
| zhaw.volume | 8 | de_CH |
| zhaw.embargo.end | 2025-01-31 | de_CH |
| zhaw.publication.review | Peer review (Publikation) | de_CH |
| zhaw.webfeed | Photovoltaik | de_CH |
| zhaw.author.additional | No | de_CH |
| zhaw.display.portrait | Yes | de_CH |
| Appears in collections: | Publikationen School of Engineering | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 2024_Zbinden-etal_Performance-limiting-parameters-in-PSCs_submitted.pdf | Submitted Version | 6.86 MB | Adobe PDF |  View/Open |
| 2024_Zbinden-etal_Performance-limiting-parameters-in-PSCs_accepted.pdf Until 2025-01-31 | Accepted Version | 14.48 MB | Adobe PDF | View/Open |
Show simple item record
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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