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.

Similarities and dissimilarities between biomolecular networks cannot be intuitively recognized even after the development of several comparison algorithms because of the lack of visualization tools. In this paper, an integrated tool kit named Biomolecular Network Match (BNMatch) is designed and developed based on Cytoscape—a popular and open-source tool for analyzing and visualizing networks. BNMatch integrates the comparison of the outputs of algorithms used for processing biomolecular networks and expresses the matching data between them by defining similar vertices and links with similar attributes. Moreover, in order to maintain consistency, their counterparts in other networks change when the nodes and edges in one of the compared networks are changed. It becomes easy for users to analyze similar networks by invoking comparison algorithms and visualizing the matching data between the networks using BNMatch.