The application of machine learning (ML)-based methods to the study of thermoelectric (TE) materials is promising. Although conventional ML algorithms can achieve high prediction performance, their lack of interpretability severely obstructs researchers from extracting material-oriented insights from ML models. In this work, high ML-based prediction performance was achieved with respect to TE power factors (PFs), and the results were well understood by the SHapley Additive exPlanations (SHAP), a method to identify the correlations between targets and descriptors. We designed a robust PF prediction model for diamond-like compounds via a stacking technique, and the model achieved a coefficient of determination value above 0.95 on the test set. From the SHAP analysis, the PFs were negatively correlated with electronegativity and positively correlated with the descriptor “volume per atom” based on the previously reported dataset. TE domain knowledge was adopted to understand these correlations. This work shows that ML models can achieve high accuracy while exhibiting good interpretability, making them useful for materials scientists.

基于机器学习 (ML) 的方法在热电 (TE) 材料研究中的应用前景广阔。尽管传统的 ML 算法可以实现高预测性能，但它们缺乏可解释性严重阻碍了研究人员从 ML 模型中提取面向材料的见解。在这项工作中，在 TE 功率因数 (PF) 方面实现了基于 ML 的高预测性能，并且 SHapley Additive exPlanations (SHAP) 很好地理解了结果，这是一种识别目标和描述符之间相关性的方法。我们通过堆叠技术设计了一个稳健的类金刚石化合物 PF 预测模型，该模型在测试集上实现了高于 0.95 的决定系数值。从 SHAP 分析，根据先前报道的数据集，PFs 与电负性呈负相关，与描述符“每个原子的体积”呈正相关。采用 TE 领域知识来理解这些相关性。这项工作表明，ML 模型可以实现高精度，同时表现出良好的可解释性，使其对材料科学家有用。