ArticleMaterials (Basel, Switzerland)2025
Data-Driven Optimization and Mechanical Assessment of Perovskite Solar Cells via Stacking Ensemble and SHAP Interpretability.
Article in Materials (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Formation Energy Prediction and Feature Analysis of Lead-Free Double Perovskites Based on Deep Learning.Molecules (Basel, Switzerland) · 2026Article
- Machine Learning-Driven Advances in Perovskite Materials and Solar Cells.Nanomaterials (Basel, Switzerland) · 2026Review
- Artificial Intelligence for Perovskite Additive Engineering: From Molecular Screening to Autonomous Discovery.Molecules (Basel, Switzerland) · 2026Review
- Enhanced Stability and Performance of α-FAPbIMaterials (Basel, Switzerland) · 2025Article
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Authors and funding
4 authors.
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Abstract
Perovskite solar cells (PSCs) have emerged as promising photovoltaic technologies owing to their high power conversion efficiency (PCE) and material versatility. Conventional optimization of PSC architectures largely depends on iterative experimental approaches, which are often labor-intensive and time-consuming. In this study, a data-driven modeling strategy is introduced to accelerate the design of efficient and mechanically robust PSCs. Seven supervised regression models were evaluated for predicting key photovoltaic parameters, including PCE, short-circuit current density (Jsc), open-circuit voltage (Voc), and fill factor (FF). Among these, a stacking ensemble framework exhibited superior predictive accuracy, achieving an R
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