Unveiling dominant recombination loss in perovskite solar cells with a XGBoost-based machine learning approach

Basir Akbar; Hilal Tayara; Kil To Chong

doi:10.1016/j.isci.2024.109200

Unveiling dominant recombination loss in perovskite solar cells with a XGBoost-based machine learning approach

iScience. 2024 Feb 12;27(3):109200. doi: 10.1016/j.isci.2024.109200. eCollection 2024 Mar 15.

Authors

Basir Akbar¹, Hilal Tayara², Kil To Chong^{3

4}

Affiliations

¹ Graduate School of Integrated Energy-AI, Jeonbuk National University, Jeonju 54896, South Korea.
² School of International Engineering and Science, Jeonbuk National University, Jeonju 54896, South Korea.
³ Advances Electronics and Information Research Center, Jeonbuk National University, Jeonju 54896, South Korea.
⁴ Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, South Korea.

Abstract

Remarkable and intelligent perovskite solar cells (PSCs) have attracted substantial attention from researchers and are undergoing rapid advancements in photovoltaic technology. These developments aim to create highly efficient energy devices with fewer dominant recombination losses within the realm of third-generation solar cells. Diverse machine learning (ML) algorithms implemented, addressing dominant losses due to recombination in PSCs, focusing on grain boundaries (GBs), interfaces, and band-to-band recombination. The extreme gradient boosting (XGBoost) classifier effectively predicts the recombination losses. Our model trained with 7-fold cross-validation to ensure generalizability and robustness. Leveraging Optuna and shapley additive explanations (SHAP) for hyperparameter optimization and investigate the influence of features on target variables, achieved 85% accuracy on over 2 million simulated data, respectively. Because of the input parameters (light intensity and open-circuit voltage), the performance evaluation measures for the dominant losses caused by the recombination predicted by proposed model were superior to those of state-of-the-art models.

Keywords: Electronic engineering; Energy application; Machine learning.