Srivastava M, Hering AR, An Y, Correa-Baena JP, Leite MS. Machine Learning Enables Prediction of Halide Perovskites' Optical Behavior with >90% Accuracy.
ACS ENERGY LETTERS 2023;
8:1716-1722. [PMID:
37090172 PMCID:
PMC10112389 DOI:
10.1021/acsenergylett.2c02555]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Accepted: 01/09/2023] [Indexed: 05/03/2023]
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 y FA1-y Pb(Br x 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.
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