Machine learning-driven prediction of organic solar cell performance: a data-centric approach to molecular design.

contextOrganic solar cells (OSCs) offer a promising route toward flexible and sustainable energy technologies, yet predictive modeling of device parameters remains challenging due to the chemical diversity of donor-acceptor systems and morphology-dependent effects. In this work, we present the first systematic demonstration of using autoencoder-compressed molecular fingerprints with tree-based machine learning models to predict key OSC performance metrics-power conversion efficiency (PCE), open-circuit voltage (V

methodsThe dataset used in this work comprises approximately 2500 experimentally characterized donor-acceptor pairs from bulk heterojunction OSCs. These include both fullerene and non-fullerene acceptor systems. For each pair, the database provides electronic descriptors, polymerization-related metrics, and the SMILES representations of the donor and acceptor molecules. Molecular fingerprints were computed from SMILES codes using the RDKit and CDK cheminformatics toolkits. A variety of machine learning models were explored, including feedforward neural networks, autoencoders for feature compression, tree-based ensemble methods, and kernel-based regression algorithms. Hyperparameter tuning was carried out using the Optuna and BayesSearchCV libraries to ensure optimal model performance.