Closed‐Loop Multi‐Objective Bayesian Optimization of High‐Dimensional Processing Spaces for Organic Solar Cells

Experience‐driven optimization has been pivotal in advancing organic photovoltaics, where chemically intuitive and stepwise tuning of processing parameters can yield high‐performance organic solar cells (OSCs). However, locating optimal regions under high‐dimensional, coupled, and multi‐objective constraints remains challenging across diverse scenarios. Here, we introduce a closed‐loop workflow base on multi‐objective Bayesian optimization for efficient optimization of OSCs. Applied to a quaternary system comprising the polymer donors PM6 and D18 and the non‐fullerene acceptors BTP‐eC9 and L8BO, the workflow navigates an eight‐dimensional space defined by composition and fabrication variables, encompassing 2.2 × 10 14 possible combinations. Within five active‐learning cycles, the search converges to a high‐performance region, achieving the efficiency exceeding 20%. The generalizability of this workflow is further demonstrated across different materials and fabrication methods, guiding the optimization toward high‐performance regions within few iterations. This work establishes an efficient and scalable closed‐loop framework for rapidly identifying favorable processing windows in organic photovoltaics.