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 ∼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 potentially extended to other modalities, such as Raman or FTIR, providing a framework for autonomous decision-making in SDLs.