用光谱预测导电聚合物导电性，减少实验次数33%。
InSpecLearn4SDL: Interpretable Spectral Features Predict Conductivity in Self-Driving Doped Conjugated Polymer Labs
- 结合遗传算法与自适应曲线下面积计算，自动提取光谱特征。
- 模型准确预测导电性，实验需求减少约33%。
- 可解释性强，适用于Raman/FTIR等其他光谱模态。
为加速自驱动实验室（SDL）中的材料发现，我们提出一种机器学习流程，通过快速、非破坏性的光学光谱技术预测掺杂共轭聚合物的电导率。该方法利用遗传算法与自适应曲线下面积（AUC）计算，实现光谱特征的自动化提取，建立光学响应与制备参数到导电性的定量结构-性能关系（QSPR）。通过引入SHAP引导的选择与基于领域知识的特征扩展，模型达到专家标注水平的性能，理论上可减少约33%的实验工作量，避免昂贵的直接导电性测量。模型在pBTTT中复现了已知物理描述符，并识别出与成功掺杂后聚合物褪色相关的特征尾态区域。该通用、可解释、小数据友好的方法可推广至拉曼或FTIR等其他光谱模态，为SDL提供自主决策框架。
原文摘要 · Abstract (English)
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.
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