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

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 optimizati

SUBMITTER: Akbar B 

PROVIDER: S-EPMC10901077 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature