Interpretable Machine Learning Workflow for Evaluating and Analyzing the Performance of High-Entropy GeTe-Based Thermoelectric Materials
Author:
Affiliation:
1. Aeronautical Engineering College, Civil Aviation University of China, Tianjin 300300, China
2. International Business School, Beijing Foreign Studies University, Beijing 100089, China
Funder
Natural Science Foundation of Tianjin City
Publisher
American Chemical Society (ACS)
Subject
Materials Chemistry,Electrochemistry,Electronic, Optical and Magnetic Materials
Link
https://pubs.acs.org/doi/pdf/10.1021/acsaelm.3c00692
Reference42 articles.
1. Materials informatics for the screening of multi-principal elements and high-entropy alloys
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