InSpecLearn4SDL: Interpretable Spectral Features Predict Conductivity in Self-Driving Doped Conjugated Polymer Labs

Abstract

To accelerate materials discovery using self-driving labs (SDLs), we present a machine learning pipeline that predicts the electrical conductivity of doped conjugated polymers using rapid, non-destructive optical spectroscopy. Our approach automates spectral featurization by combining a genetic algorithm with adaptive area-under-the-curve (AUC) computations, creating a quantitative structure-property relationship (QSPR) that links optical response and processing parameters to conductivity. By incorporating SHAP-guided selection and domain-knowledge-based feature expansion, the model matches expert-curated performance while theoretically reducing experimental effort by $\sim 33\%$ by minimizing the need for costly direct conductivity measurements. Notably, the model recovers known physical descriptors in pBTTT and identifies informative tail-state regions correlated with polymer bleaching upon successful doping. This generic, interpretable, small-data-friendly methodology can be extended to other spectroscopic modalities, such as Raman or FTIR, providing a framework for autonomous decision-making in SDLs.