Work

Bayesian optimization and prediction of the durability of triple-halide perovskite thin films under light and heat stressors

Year: 2024

Type: article

Abstract: 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.

Source: Materials Advances

DOI: 10.1039/d4ma00747f

Authors: Deniz N. Cakan, Eric Oberholtz, Ken Kaushal, Sean P. Dunfield, David P. Fenning

Institution: University of California San Diego

Language: English

Topic: Perovskite Materials and Applications

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Affordable and clean energy

Open Access status: gold

Estimated APC paid: $1,962

Funders: U.S. Department of Energy, California Energy Commission, Solar Energy Technologies Office

Awards: G3491840229, G3816276605, G8834858196

OpenAlex ID: W4404572676

FWCI: 1.743

Cites: 56

Cited by: 17

Related to: 10