Machine learning aided optoelectric characterization modelling and prediction of the IV parameters of perovskite solar cells with > 90% accuracy.

ABSTRACT: Luminescence imaging techniques assume a critical role in the evaluation of the durability and lifespan of solar cells. This study presents a novel empirical approach that capitalizes on simple machine learning-based linear regression (MLLR) to prognosticate the performance of perovskite solar cells (PSCs). Three types of MLLR models were developed: (a) Full width at Half maximum (FWHM)-based MLLR Model, (b) Colour Correlated Temperature (CCT)-based MLLR Model, and (c) Hybrid or FWHM-CCT-based MLLR Model. The proposed FWHM-based MLLR model achieves the best overall performance in fitting all current-voltage (IV) parameters. The CCT-based model, while applicable to all parameters, exhibits slightly lower accuracy compared to the FWHM-based approach; however, considering standalone analysis,

SUBMITTER: Kumar J 

PROVIDER: S-EPMC12368217 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature