Document Type

Article

Publication Date

2024

DOI

10.3390/ma17194754

Publication Title

Materials

Volume

17

Issue

19

Pages

4754 (1-8)

Abstract

This work applied three machine learning (ML) models—linear regression (LR), random forest (RF), and support vector regression (SVR)—to predict the lattice parameters of the monoclinic B19′ phase in two distinct training datasets: previously published ZrO₂-based shape-memory ceramics (SMCs) and NiTi-based high-entropy shape-memory alloys (HESMAs). Our findings showed that LR provided the most accurate predictions for ac, am, bm, and cm in NiTi-based HESMAs, while RF excelled in computing βm for both datasets. SVR disclosed the largest deviation between the predicted and actual values of lattice parameters for both training datasets. A combination approach of RF and LR models enhanced the accuracy of predicting lattice parameters of martensitic phases in various shape-memory materials for stable high-temperature applications.

Rights

© 2024 by the authors.

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International (CC BY 4.0) License.

Data Availability

Article states: "The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding authors."

Original Publication Citation

Lam, T. N., Jiang, J., Hsu, M. C., Tsai, S. R., Luo, M. Y., Hsu, S. T., Lee, W. J., Chen, C. H., & Huang, E. W. (2024). Predictions of lattice parameters in NiTi high-entropy shape-memory alloys using different machine learning models. Materials 17(19), 1-8, Article 4754. https://doi.org/10.3390/ma17194754

ORCID

0000-0003-2958-5666 (Jiang)

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