Accurate and explainable machine learning for the power factors of diamond-like thermoelectric materials  

作　　者：Zhe Yang Ye Sheng Cong Zhu Jianyue Ni Zhenyu Zhu Jinyang Xi Wu Zhang Jiong Yang 

机构地区：[1]Materials Genome Institute,Shanghai University,Shanghai,200444,China [2]School of Computer Engineering and Science,Shanghai University,Shanghai,200444,China [3]School of Mechanics and Engineering Science,Shanghai University,Shanghai,200444,China

出　　处：《Journal of Materiomics》2022年第3期633-639,共7页无机材料学学报（英文）

基　　金：The work of this paper was supported by the National Key R&D Programs of China(No.2017YFB0701501 and 2018YFB0703600).

摘　　要：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.

关 键 词：Machine learning Thermoelectric materials INTERPRETABILITY Power factor 

分 类 号：TB33[一般工业技术—材料科学与工程] TP181[自动化与计算机技术—控制理论与控制工程]