Interpretable machine learning workflow for evaluation of the transformation temperatures of TiZrHfNiCoCu high entropy shape memory alloys

Interpretable machine learning workflow for evaluation of the transformation temperatures of TiZrHfNiCoCu high entropy shape memory alloys

Machine learning approaches (ML) based on data-driven models are conducive to accelerating the assessments of the martensitic transformation peak temperature (Tp) of TiZrHfNiCoCu high entropy shape memory alloys (HESMAs) over a huge composition space. In this work, an interpretable machine learning...

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Bibliographic Details
Main Authors: Shiyu He, Yanming Wang, Zhengyang Zhang, Fei Xiao, Shungui Zuo, Ying Zhou, Xiaorong Cai, Xuejun Jin
Format: Article
Language:English
Published: Elsevier 2023-01-01
Series:Materials & Design
Subjects:
High entropy shape memory alloys
Machine learning
SHAP explanation
Feature selection
Transformation temperature
Online Access:http://www.sciencedirect.com/science/article/pii/S0264127522011364

Internet

http://www.sciencedirect.com/science/article/pii/S0264127522011364

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