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

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

doi:10.1039/D4MA00747F

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

Journal Article · 20 January 2025 · Materials Advances

DOI: https://doi.org/10.1039/D4MA00747F · OSTI ID:2480962

^[1]; Oberholtz, Eric ^[1]; Kaushal, Ken ^[2];

^[1];

^[2]

Aiiso Yufeng Li Family Department of Chemical and Nano Engineering, University of California San Diego, La Jolla, CA 92093, USA
Aiiso Yufeng Li Family Department of Chemical and Nano Engineering, University of California San Diego, La Jolla, CA 92093, USA, Materials Science & Engineering Program, University of California San Diego, La Jolla, CA 92093, USA

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.

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Sponsoring Organization:: USDOE

Grant/Contract Number:: EE0010503

OSTI ID:: 2480962

Journal Information:: Materials Advances, Journal Name: Materials Advances Journal Issue: 2 Vol. 6; ISSN MAADC9; ISSN 2633-5409

Publisher:: Royal Society of Chemistry (RSC)Copyright Statement

Country of Publication:: United Kingdom

Language:: English

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