Interpretable Spectral Features Predict Conductivity in Self-Driving Doped Conjugated Polymer Labs

• URL: http://arxiv.org/abs/2509.21330v1
• Date: Sat, 06 Sep 2025 18:00:40 GMT
• Title: Interpretable Spectral Features Predict Conductivity in Self-Driving Doped Conjugated Polymer Labs
• Authors: Ankush Kumar Mishra, Jacob P. Mauthe, Nicholas Luke, Aram Amassian, Baskar Ganapathysubramanian,
• Abstract summary: Self-driving labs promise faster materials discovery by coupling automation with machine learning.<n>We address this by learning interpretable spectral fingerprints from optical spectroscopy to predict electrical conductivity.
• Score: 2.8914750842461583
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Self-driving labs (SDLs) promise faster materials discovery by coupling automation with machine learning, but a central challenge is predicting costly, slow-to-measure properties from inexpensive, automatable readouts. We address this for doped conjugated polymers by learning interpretable spectral fingerprints from optical spectroscopy to predict electrical conductivity. Optical spectra are fast, non-destructive, and sensitive to aggregation and charge generation; we automate their featurization by combining a genetic algorithm (GA) with area-under-the-curve (AUC) computations over adaptively selected spectral windows. These data-driven spectral features, together with processing parameters, are used to train a quantitative structure-property relationship (QSPR) linking optical response and processing to conductivity. To improve accuracy and interpretability in the small-data regime, we add domain-knowledge-based feature expansions and apply SHAP-guided selection to retain a compact, physically meaningful feature set. The pipeline is evaluated under a leak-free train/test protocol, and GA is repeated to assess feature stability. The data-driven model matches the performance of a baseline built from expert-curated descriptors while reducing experimental effort (about 33%) by limiting direct conductivity measurements. Combining data-driven and expert features yields a hybrid QSPR with superior predictive performance, highlighting productive human-ML collaboration. The learned features recover known descriptors in pBTTT (0-0/0-1 vibronic intensity ratio) and reveal a tail-state region correlated with polymer bleaching during successful doping. This approach delivers interpretable, noise-robust, small-data-friendly features that convert rapid measurements into reliable predictions of costly properties and readily extends to other spectral modalities (e.g., XANES, Raman, FTIR).

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