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Magnetic Shape Memory Alloys: Fundamentals and Applications

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Dr. Zhenzhuang Li

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Guest Editor

School of Material Science and Engineering, Dalian Jiaotong University, Dalian 116028, China
Interests: shape memory alloy; martensitic transformation; elastocaloric effect; magnetocaloric effect; thermal energy storage

Dr. Ziqi Guan

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Guest Editor

Shenyang National Laboratory for Materials Science, Institute of Metal Research, Chinese Academy of Sciences, Shenyang 110016, China
Interests: martensitic transformation; shape memory alloys; magnetocaloric effect; elastocaloric effect; barocaloric effect

Special Issue Information

Dear Colleagues,

The fundamentals and applications of shape memory alloys in relation to solid-state refrigeration have been widely reported in recent years. In view of your expertise in the field, we invite you to submit your research to this Special Issue of Materials, entitled “Magnetic Shape Memory Alloys: Fundamentals and Applications”. The Special Issue aims to promote the development of magnetic shape memory alloy solid-state refrigeration. We believe that your research results will greatly increase the impact of this research field.

In this Special Issue, original research articles and reviews are welcome. Research areas may include the following: magnetic shape memory alloys, martensitic transformation, magnetic properties, magnetocaloric effect, elastocaloric effect, barocaloric effect, and thermal energy storage.

We look forward to receiving your contributions.

Dr. Zhenzhuang Li
Dr. Ziqi Guan
Guest Editors

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15 pages, 6829 KB

Open AccessArticle

Thermal Hysteresis and Reversibility of the Giant Magnetocaloric Effect at the Ferromagnetic Transition of Nd₂In

by Bao Gegen, Bao Huhe, Zhi-Qiang Ou, Francois Guillou and Hargen Yibole

Materials 2025, 18(13), 3104; https://doi.org/10.3390/ma18133104 - 1 Jul 2025

Viewed by 365

Abstract

The Nd₂In compound exhibits an intriguing borderline first-/second-order transition at its Curie temperature. Several studies have pointed to its potential for magnetic cooling, but also raised controversies about the actual order of the transition, the amplitudes of the hysteresis, and of its magnetocaloric effect. Here, we estimate the thermal hysteresis using magnetic and thermal measurements at different rates. It is found to be particularly small (0.1–0.4 K), leading to almost fully reversible adiabatic temperature changes when comparing zero-field cooling and cyclic protocols. Some open questions remain with regard to the magnetostriction of Nd₂In, which is presently found to be limited, in line with the absence of a thermal expansion discontinuity at the transition. The comparison of the magnetocaloric effect in Nd₂In and Eu₂In highlights that the limited saturation magnetization of the former affects its performance. Further efforts should therefore be made to design materials with such borderline first-/second-order transitions using heavier rare earths. Full article

(This article belongs to the Special Issue Magnetic Shape Memory Alloys: Fundamentals and Applications)

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13 pages, 2728 KB

Open AccessArticle

Machine Learning-Assisted Discovery of Empirical Rule for Martensite Transition Temperature of Shape Memory Alloys

by Hao-Xuan Liu, Hai-Le Yan, Nan Jia, Bo Yang, Zongbin Li, Xiang Zhao and Liang Zuo

Materials 2025, 18(10), 2226; https://doi.org/10.3390/ma18102226 - 12 May 2025

Viewed by 555

Abstract

Shape memory alloys (SMAs) derive their unique functional properties from martensitic transformations, with the martensitic transformation temperature (T_M) serving as a key design parameter. However, existing empirical rules, such as the valence electron concentration (VEC) and lattice volume (V) criteria, are typically restricted to specific alloy families and lack general applicability. In this work, we used a data-driven methodology to find a generalizable empirical formula for T_M in SMAs by combining high-throughput first-principles calculations, feature engineering, and symbol regression techniques. Key factors influencing T_M were first identified and a predictive machine learning model was subsequently trained based on these features. Furthermore, an empirical formula of T_M =

82 (\bar{ρ} \cdot \bar{M P}) - 700

was derived, where

\bar{ρ}

and

\bar{M P}

represent the weight-average value of density and melting point, respectively. The empirical formula exhibits strong generalizability across a wide range of SMAs, such as NiMn-based, NiTi-based, TiPt-based, and AuCd-based SMAs, etc., offering practical guidance for the compositional design and optimization of shape memory alloys. Full article

(This article belongs to the Special Issue Magnetic Shape Memory Alloys: Fundamentals and Applications)

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