Identifying a Figure of Merit for Solvent Optimization in Non-Fullerene Organic Solar Cells via High-Throughput Experiments.

Solvent optimization in organic solar cells (OSCs) remains largely empirical, requiring repeated device fabrication and characterization across a broad formulation space. Here, we introduce a high-throughput workflow to decipher a figure of merit (FoM) for solvent optimization of non-fullerene acceptor (NFA)-based OSCs. Using D18:L8BO as a representative system, we prepare a diverse solvent formulation via high-throughput experimentation and in situ optical characterization during film formation. Machine learning analysis unveils the initial donor-to-acceptor absorption ratio during deposition (I_D/A) as a key FoM, which enables the efficient optimization of solvents in OSCs. Comprehensive characterization identifies the photophysical and morphological properties associated with the optimized formulation. Evaluation across several polymer:Y-series NFA systems further supports I_D/A as a FoM for solvent optimization. This study establishes an experimentally accessible strategy that integrates the exploration of broad solvent formulation spaces with targeted device validation into a targeted, data-driven workflow, offering a practical route for accelerating OSC development.