Database for multi-tier machine learning optimization of organic photovoltaic photoactive layer fabrication

This dataset provides a standardized, multi-tiered resource for optimizing organic photovoltaic devices through data-driven methods. It integrates donor/acceptor molecular structures (with SMILES, CDK fingerprints, and electronic descriptors) with nine key photoactive layer processing parameters and device efficiency.
The data is uniquely structured in three tiers to guide optimization: from single-parameter analysis, through stage-combined synergies (solution preparation, spin-coating, post-processing), to global optimization with all parameters. This structure facilitates the development and validation of both interpretable and high-performance machine learning models.
For complete details on the dataset structure, file descriptions, and exact data dictionary, please refer to the README.txt file included in the download.