Navigating high-dimensional processing parameters in organic photovoltaics via a multi-tier machine learning framework

Abstract

Organic photovoltaics (OPV) have achieved significant advances over recent decades, driven by synergistic innovations in molecular design and device engineering. However, precise morphological control within bulk-heterojunction active layers remains a crucial barrier to commercial viability, primarily due to the high-dimensional parameter spaces and complex interdependencies among processing variables. To overcome this challenge, we established a standardized materials-processing-performance database integrating donor/acceptor pairs, nine key active layer processing parameters, and device efficiencies. This database, curated from over a decade of experimental results, resolves critical data heterogeneity issues and provides the field's most comprehensive optimization resource. Leveraging this resource, we developed a novel three-tiered machine learning framework employing gradient boosting regression trees to progressively decode active layer processing complexities. Our strategy initiates with single-parameter models for targeted optimization, advances through stage-combined models revealing intra-process synergies (e.g., solvent-additive interplay), and culminates in a global optimization tier. Remarkably, this final tier achieves unprecedented performance, demonstrating >0.9 overall Pearson correlations, and >80% success rates in identifying optimal nine-dimensional configurations. Extrapolation validation on 78 novel systems, wherein each system contains a structurally novel donor or acceptor, confirms the model's exceptional generalization capability, yielding >75% accuracy in predicting either optimal or secondary parameters across eight active layer processing conditions. This work establishes a robust framework for navigating processing complexity in high-dimensional spaces, enabling accelerated optimization of OPV photoactive layers and providing a transferable data-driven paradigm for rational process design in emerging photovoltaic technologies.

Affiliated Institutions

• Shandong University CN
• Henan Normal University CN

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