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.