Gao, Y.; Hu, Y.; Zhao, X.; Liu, Y.; Huang, H.; Su, Y. Machine Learning-Driven Design of High-Elastocaloric NiTi-Based Shape Memory Alloys. Preprints2024, 2024081415. https://doi.org/10.20944/preprints202408.1415.v1
APA Style
Gao, Y., Hu, Y., Zhao, X., Liu, Y., Huang, H., & Su, Y. (2024). Machine Learning-Driven Design of High-Elastocaloric NiTi-Based Shape Memory Alloys. Preprints. https://doi.org/10.20944/preprints202408.1415.v1
Chicago/Turabian Style
Gao, Y., Haiyou Huang and Yanjing Su. 2024 "Machine Learning-Driven Design of High-Elastocaloric NiTi-Based Shape Memory Alloys" Preprints. https://doi.org/10.20944/preprints202408.1415.v1
Abstract
In recent years, due to the detrimental impact of traditional gas-liquid refrigerants on the environment, the pursuit of solid-state refrigeration technology based on elastocaloric effect, as a promising sustainable alternative, has been promoted. However, the traditional trial-and-error method is inefficient and difficult to meet the material application requirements. In this work, machine learning (ML) was employed to accelerate the development of NiTi-based shape memory alloy (SMA) with excellent elastocaloric effect. By means of active learning, we identified 9 novel NiTi-based SMAs in four iterations, the phase transformation-induced entropy change (ΔS) of which are as high as over 90 J/kg·K-1. Moreover, the model exhibits good interpretability, allowing us to understand the relevance between the target and features from the aspect of mechanism.
Chemistry and Materials Science, Materials Science and Technology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
