Machine learning-enhanced novel design and performance optimization of M 3 SbI 3 (M = Ba and Ca) based dual absorber perovskite solar cells

RSC Advances 2026 Open access

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Authors: Md. Anwer Hossain, Md Rasidul Islam, A. M. Quraishi, Sobhi M. Gomha, Magdi E. A. Zaki, MA Rana
DOI: 10.1039/d6ra00497k
Cited by: 3
References: 79

Abstract

) of 0.9635, mean absolute error (MAE) of 0.4506, and root mean square error (RMSE) of 0.6253. Moreover, feature importance analysis validated by SHAP (Shapley Additive exPlanations) summary plots and correlation matrices revealed that operating temperature and absorber layer parameters (doping, thickness, and defect level) were the most critical factors influencing photovoltaic performance, while electron and hole transport layer properties also played significant roles. These outcomes reveal that the proposed lead-free DAPSC model presents enhanced performance, stability, and environmental compatibility for photovoltaic applications.

Fields of Study & Subjects

Perovskite Materials and Applications Machine Learning in Materials Science Heusler alloys: electronic and magnetic properties

Publication details

Journal / source
RSC Advances
Publisher
Royal Society of Chemistry (RSC)
Volume
16
Issue
15
Pages
13162–13182
Publication date
1 January 2026
Language
EN