1. Machine Learning Enables Prediction of Halide Perovskites' Optical Behavior with >90% Accuracy.
- Author
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Srivastava M, Hering AR, An Y, Correa-Baena JP, and Leite MS
- Abstract
The composition-dependent degradation of hybrid organic-inorganic perovskites (HOIPs) due to environmental stressors still precludes their commercialization. It is very difficult to quantify their behavior upon exposure to each stressor by exclusively using trial-and-error methods due to the high-dimensional parameter space involved. We implement machine learning (ML) models using high-throughput, in situ photoluminescence (PL) to predict the response of Cs
y FA1- y Pb(Brx I1- x )3 while exposed to relative humidity cycles. We quantitatively compare three ML models while generating forecasts of environment-dependent PL responses: linear regression, echo state network, and seasonal autoregressive integrated moving average with exogenous regressor algorithms. We achieve accuracy of >90% for the latter, while tracking PL changes over a 50 h window. Samples with 17% of Cs content consistently showed a PL increase as a function of cycle. Our precise time-series forecasts can be extended to other HOIP families, illustrating the potential of data-centric approaches to accelerate material development for clean-energy devices., Competing Interests: The authors declare no competing financial interest., (© 2023 The Authors. Published by American Chemical Society.)- Published
- 2023
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