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Here, we introduce a high\u2010throughput workflow to decipher a figure of merit (FoM) for solvent optimization of non\u2010fullerene acceptor (NFA)\u2010based OSCs. Using D18:L8BO as a representative system, we prepare a diverse solvent formulation via high\u2010throughput experimentation and in situ optical characterization during film formation. Machine learning analysis unveils the initial donor\u2010to\u2010acceptor 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\u2010series 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\u2010driven workflow, offering a practical route for accelerating OSC development.<\/jats:p>","DOI":"10.1002\/adma.75344","type":"journal-article","created":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T07:58:07Z","timestamp":1791532687000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Identifying a Figure of Merit for Solvent Optimization in Non\u2010Fullerene Organic Solar Cells via High\u2010Throughput Experiments"],"prefix":"10.1002","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8986-9874","authenticated-orcid":false,"given":"Kang","family":"An","sequence":"first","affiliation":[{"name":"Institute of Polymer Optoelectronic Materials and Devices Guangdong Basic Research Center of Excellence for Energy and Information 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