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

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

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 fro...

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Bibliographic Details
Main Authors: Zhe Yang, Ye Sheng, Cong Zhu, Jianyue Ni, Zhenyu Zhu, Jinyang Xi, Wu Zhang, Jiong Yang
Format: Article
Language:English
Published: Elsevier 2022-05-01
Series:Journal of Materiomics
Subjects:
Machine learning
Thermoelectric materials
Interpretability
Power factor
Online Access:http://www.sciencedirect.com/science/article/pii/S235284782100160X

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http://www.sciencedirect.com/science/article/pii/S235284782100160X

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