Solar energy paper index
High-Dimensional Physics-Guided Machine Learning for Interpretable Optimization of Lead-Free Cs2AgBi0.75Sb0.25Br6 Double-Perovskite Solar Cells
One-line summary
A solar energy research paper on High-Dimensional Physics-Guided Machine Learning for Interpretable Optimization of Lead-Free Cs2AgBi0.75Sb0.25Br6 Double-Perovskite Solar Cells.
Engineering notes
Engineering notes will be added by the Power for Solar editorial team.
Chinese explanation / 中文解读
中文解读待补充：本站会优先为光伏效率、钙钛矿太阳能电池、储能技术、太阳能热利用、BIPV、并网技术等高价值论文补充中文说明。
Original abstract
This release contains the Jupyter Notebook, raw and preprocessed SCAPS-1D simulation datasets, machine-learning cross-validation results, and actual-versus-predicted data associated with the manuscript: "High-Dimensional Physics-Guided Machine Learning for Interpretable Optimization of Lead-Free Cs2AgBi0.75Sb0.25Br6 Double-Perovskite Solar Cells." The repository supports reproducibility of the data preprocessing, model training, cross-validation, performance evaluation, interpretability analysis, and optimization workflow.
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