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<p>Computational design of high-efficiency organic photovoltaics requires clear links between three-dimensional active-layer morphology and device performance. We present a data-driven workflow that first uses coreset selection to distill a large library of simulated morphologies into a small, representative subset, thereby focusing expensive morphology-aware exciton drift–diffusion simulations where they matter most. Using two device performance metrics, short-circuit current density, <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(J_{\textrm{SC}}\)</EquationSource> <EquationSource Format="MATHML"><math> <msub> <mi>J</mi> <mtext>SC</mtext> </msub> </math></EquationSource> </InlineEquation>, and fill factor, FF, from these simulations, we then apply feature-selection strategies to identify a handful of interpretable morphological descriptors that accurately predict both quantities. Sample-size ablations show that model accuracy, and the identity and rankings of the selected descriptors, remain stable with as few as 50 samples with the device performance. The descriptors most predictive of <InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(J_{\textrm{SC}}\)</EquationSource> <EquationSource Format="MATHML"><math> <msub> <mi>J</mi> <mtext>SC</mtext> </msub> </math></EquationSource> </InlineEquation> differ from those for FF, reflecting distinct morphological bases for these two performance metrics. Moreover, cross-system comparisons (P3HT:PCBM vs. PM6:Y6) reveal shifts in the most influential descriptors, indicating that morphology–performance relationships are material-specific.</p> Graphical abstract <p></p>

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Three-dimensional morphology–performance mapping for organic solar cells: A data-driven framework

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月23日

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  • Marjan Saadati,
  • Ankush Kumar Mishra,
  • Nirmal Baishnab,
  • Olga Wodo,
  • Baskar Ganapathysubramanian

摘要

Computational design of high-efficiency organic photovoltaics requires clear links between three-dimensional active-layer morphology and device performance. We present a data-driven workflow that first uses coreset selection to distill a large library of simulated morphologies into a small, representative subset, thereby focusing expensive morphology-aware exciton drift–diffusion simulations where they matter most. Using two device performance metrics, short-circuit current density, \(J_{\textrm{SC}}\) J SC , and fill factor, FF, from these simulations, we then apply feature-selection strategies to identify a handful of interpretable morphological descriptors that accurately predict both quantities. Sample-size ablations show that model accuracy, and the identity and rankings of the selected descriptors, remain stable with as few as 50 samples with the device performance. The descriptors most predictive of \(J_{\textrm{SC}}\) J SC differ from those for FF, reflecting distinct morphological bases for these two performance metrics. Moreover, cross-system comparisons (P3HT:PCBM vs. PM6:Y6) reveal shifts in the most influential descriptors, indicating that morphology–performance relationships are material-specific.

Graphical abstract