The challenge of studying perovskite solar cells’ stability with machine learning
- DOI
- 10.3389/fenrg.2023.1118654
- Published
- 2023-04-03
- Container
- Frontiers in Energy Research
- Publisher
- Frontiers Media SA
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.3389/fenrg.2023.1118654,
title = {The challenge of studying perovskite solar cells’ stability with machine learning},
author = {Paolo Graniero and Mark Khenkin and Hans Köbler and Noor Titan Putri Hartono and Rutger Schlatmann and Antonio Abate and Eva Unger and T. Jesper Jacobsson and Carolin Ulbrich},
year = {2023},
journal = {Frontiers in Energy Research},
doi = {10.3389/fenrg.2023.1118654},
url = {https://doi.org/10.3389/fenrg.2023.1118654}
}RIS
TY - JOUR TI - The challenge of studying perovskite solar cells’ stability with machine learning AU - Paolo Graniero AU - Mark Khenkin AU - Hans Köbler AU - Noor Titan Putri Hartono AU - Rutger Schlatmann AU - Antonio Abate AU - Eva Unger AU - T. Jesper Jacobsson AU - Carolin Ulbrich PY - 2023 JO - Frontiers in Energy Research DO - 10.3389/fenrg.2023.1118654 UR - https://doi.org/10.3389/fenrg.2023.1118654 ER -
APA
Graniero, P., Khenkin, M., Köbler, H., Hartono, N. T. P., Schlatmann, R., Abate, A., Unger, E., Jacobsson, T. J., & Ulbrich, C. (2023). The challenge of studying perovskite solar cells’ stability with machine learning. Frontiers in Energy Research. https://doi.org/10.3389/fenrg.2023.1118654
Source records
- crossref · retrieved 2026-09-25T12:54:57.174Z