Bayesian optimization and prediction of the durability of triple-halide perovskite thin films under light and heat stressors
A machine learning regression model robustly predicts phase instability in wide bandgap halide perovskites by linking the spectral variation in 60-second photoluminescence tests to tests under 800 h, 1-sun, 85 °C conditions.
Authors
- Sean P. Dunfield (ORCID: https://orcid.org/0000-0002-2855-4457)
- Deniz N. Cakan (ORCID: https://orcid.org/0000-0001-5177-8654)
- David P. Fenning (ORCID: https://orcid.org/0000-0002-4609-9312)
- Ken Kaushal
- Eric Oberholtz
Institutions
- University of California San Diego (US)
Publication Details
- Journal
- Materials Advances
- Published
- 2024-11-21
- DOI
- https://doi.org/10.1039/d4ma00747f
- Citations
- 13
- Primary Topic
- Perovskite Materials and Applications
- Type
- article
- Field-Weighted Citation Impact
- 2.71
Funders
- U.S. Department of Energy
- California Energy Commission
- Solar Energy Technologies Office