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Machine Learning Enables Prediction of Halide Perovskites' Optical Behavior with >90% Accuracy.
- Source :
-
ACS energy letters [ACS Energy Lett] 2023 Mar 10; Vol. 8 (4), pp. 1716-1722. Date of Electronic Publication: 2023 Mar 10 (Print Publication: 2023). - Publication Year :
- 2023
-
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 <subscript> y </subscript> FA <subscript>1- y </subscript> Pb(Br <subscript> x </subscript> I <subscript>1- x </subscript> ) <subscript>3</subscript> 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.<br />Competing Interests: The authors declare no competing financial interest.<br /> (© 2023 The Authors. Published by American Chemical Society.)
Details
- Language :
- English
- ISSN :
- 2380-8195
- Volume :
- 8
- Issue :
- 4
- Database :
- MEDLINE
- Journal :
- ACS energy letters
- Publication Type :
- Academic Journal
- Accession number :
- 37090172
- Full Text :
- https://doi.org/10.1021/acsenergylett.2c02555