Materials-Microstructure-Performance Mapping in Organic Photovoltaics

Organic photovoltaics (OPVs) require joint optimization of materials parameters and active-layer microstructure to maximize device performance, including short-circuit current, 𝐽 𝑠𝑐 . We present a material property and microstructure-aware surrogate that predicts across diverse donor-acceptor systems and microstructures while providing calibrated uncertainties and interpretable design rules. We curate a 25k-sample, physics-informed dataset by sweeping electron and hole mobilities (𝜇 𝑛 , 𝜇 𝑝 ) and exciton lifetime (𝜏 𝑥 ) across multiple microstructure classes, and derive a compact feature set that couples materials parameters with graph-based microstructure descriptors (interfacial area, and connectivity/tortuosity) to predict 𝐽 𝑠𝑐. A lightweight random-forest model achieves 𝑅 2 ≥ 0.98 with ≤ 40% of the data used for training, and maintains accuracy under material-wise holdouts (i.e., material property holdouts). Partial-dependence analyses reveal regime transitions in the coupled design space: at low mobilities and short 𝐿 𝑑 , performance is dual-transport-limited; beyond a mobility threshold, 𝜏 𝑥 (hence 𝐿 𝑑 ) and interfacial proximity dominate 𝐽 𝑠𝑐 . The framework accelerates exploration of material-microstructure trade-offs, identifies whether to intervene via material (mobilities, and 𝜏 𝑥 ) or processing (phase-separation length scale, connectivity, interfacial area), and is extensible to other organic optoelectronic systems. Code, features, and data are released to enable reproducibility and reuse.