Machine Learning Driven Analysis Yields Insight into the Processing Robustness of Organic Photovoltaics

Optimizing the processing window for organic photovoltaics (OPVs) can deliver high efficiency and yield. In practice, small drifts in solution concentration, additive fraction, drying rate, or ambient humidity readily perturb the evolving morphology, making champion devices hard to reproduce. We combine a complementary Taguchi and space-filling design-of-experiments strategy with machine-learning surrogates to map processing robustness in PM6:Y6 films using acetone and 1-chloronaphthalene as the processing additives. Robustness is quantified via parameter-space fraction (share of conditions exceeding a power conversion efficiency threshold), persistence curves (how that share contracts as the threshold tightens), and fill fraction (compactness of high-performing regions). The parameter space landscape when using acetone as a solvent additive exhibits a broad, robust regime, with around 73% of the explored space exceeding 9% PCE; this is consistent with our hypothesis that faster evaporation yields morphology tolerant to routine variability. This ML-enabled framework disentangles manufacturing-relevant robustness from peak PCE and offers a general, data-efficient way to compare processing-window robustness with reduced experimental effort. Such tools integrate naturally with self-driving laboratories and can identify new processing opportunities for OPVs.