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Metallic Functional Materials: Design, Processing, and Advanced Applications
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Metallic Functional Materials: Design, Processing, and Advanced Applications

A Special Issue of Materials (ISSN 1996-1944) belonging to the section "Metals and Alloys".

Deadline for manuscript submissions: 20 March 2027 | Viewed by 1013

Editors

Prof. Dr. Changlong Tan

E-Mail Website
Guest Editor
School of Materials Science and Chemical Engineering, Harbin University of Science and Technology, Harbin, China
Interests: functional metallic materials; shape memory alloys
Special Issues, Collections and Topics in MDPI journals
Dr. Wenbin Zhao

E-Mail Website
Guest Editor
School of Materials Science and Chemical Engineering, Harbin University of Science and Technology, Harbin, China
Interests: shape memory alloys; machine learning-assisted materials design
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Metallic functional materials exhibiting phase-transition-driven properties have attracted extensive attention due to their unique multifunctional responses and broad technological applications. Among them, shape memory alloys (SMAs), superelastic alloys, and caloric materials—including magnetocaloric, elastocaloric, and barocaloric systems—have emerged as promising candidates for solid-state refrigeration, energy-efficient actuation, intelligent sensing, aerospace systems, and biomedical devices. Their exceptional functional behaviors, originating from reversible phase transformations and strong coupling among thermal, mechanical, magnetic, and structural fields, offer new opportunities for developing next-generation smart materials and sustainable technologies.

Recent advances in computational materials science, first-principles calculations, machine learning, and materials informatics have accelerated the discovery and optimization of metallic functional materials with enhanced performance. In particular, integrating composition design, microstructure regulation, defect engineering, and data-driven approaches has enabled more efficient development of phase-transition metallic systems with reduced hysteresis, improved reversibility, low driving stress, and tunable multifunctional properties.

This Special Issue aims to provide an interdisciplinary platform for researchers to report recent advances in the design, synthesis, characterization, theoretical understanding, and engineering applications of phase-transition metallic functional materials. We particularly welcome contributions related to shape memory alloys and caloric materials, as well as emerging methodologies for intelligent materials discovery and performance optimization. Original research articles, reviews, perspectives, and communications are welcome.

Topics of interest include but are not limited to:

Shape Memory and Phase-Transition Metallic Materials

  • Shape memory alloys and superelastic metallic materials.
  • Martensitic transformation and phase-transition mechanisms.
  • High-temperature and multifunctional shape memory alloys.
  • Magnetic shape memory alloys and magnetostructural coupling.

Caloric and Energy-Relevant Functional Materials

  • Magnetocaloric, elastocaloric, barocaloric, and multicaloric materials.
  • Low-driving-field/barocaloric materials for solid-state cooling.
  • Thermal management and energy-efficient refrigeration materials.
  • Coupled-field effects and entropy engineering in metallic systems.

Intelligent Design and Characterization

  • First-principles calculations and computational design of functional metallic materials.
  • Machine learning and materials informatics for alloy discovery.
  • High-throughput screening and data-driven optimization.
  • In situ characterization and multi-scale modeling.
  • Microstructure evolution, defect engineering, and property regulation.

Processing, Reliability, and Applications

  • Advanced processing and manufacturing of metallic functional materials.
  • Reliability, degradation, fatigue, and cyclic stability.
  • Smart metallic systems and engineering applications.
  • Biomedical and aerospace applications of functional alloys.

We encourage submissions addressing fundamental mechanisms, innovative material systems, advanced characterization methods, and emerging applications. Contributions integrating experimental studies with computational modeling or machine learning-assisted materials design are particularly welcome.

This Special Issue aims to advance the understanding and development of shape memory and caloric metallic materials while promoting interdisciplinary collaboration among materials scientists, physicists, chemists, and engineers.

Prof. Dr. Changlong Tan
Dr. Wenbin Zhao
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Materials is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • shape memory alloys
  • phase-transition metallic materials
  • caloric materials
  • machine learning-assisted materials design
  • functional alloy engineering

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Published Papers (2 papers)

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Research

18 pages, 15674 KB  
Article
Study on Residual Stresses and Deformations in Turning of Aerospace Thin-Web Gears Considering the Initial Heat-Treatment State and Clamping Constraints
by Tao Chen, Shengwei Tong, Wenyao Wang, Suyan Li, Wenyuan Xu, Lankui Su, Hao Sun and Catherine Sotova
Materials 2026, 19(14), 3039; https://doi.org/10.3390/ma19143039 - 14 Jul 2026
Viewed by 350
Abstract
As transmission systems evolve toward lightweight design, gear webs are becoming thinner and more sensitive to deformation caused by the coupled action of heat-treatment residual stress, finish-turning thermo-mechanical loading, and clamping constraints. Existing studies mainly treat the initial residual stress or the machining-induced [...] Read more.
As transmission systems evolve toward lightweight design, gear webs are becoming thinner and more sensitive to deformation caused by the coupled action of heat-treatment residual stress, finish-turning thermo-mechanical loading, and clamping constraints. Existing studies mainly treat the initial residual stress or the machining-induced residual stress separately and often simplify the clamping boundary as an ideal fixed constraint. To overcome these limitations, this study proposes an initial-field-driven prediction framework for aerospace thin-web gears. The post-heat-treatment residual stress/strain field is reconstructed using the eigenstrain reconstruction method using measured residual stress and deformation data and is then introduced into the ABAQUS finish-turning model as the actual initial state. A three-jaw-chuck boundary consistent with the experiment and a progressive element birth–death strategy driven by the measured cutting force and temperature are used to describe material removal. In addition, a laser displacement sensor on-machine measurement (LOMM) method is developed for initial pose correction, deformation monitoring, and clamping-force interval optimization. The predicted final residual stress (FRS) distribution and machining deformation agree with the experimental measurements, with errors generally below 10%. The optimized clamping-force interval of 1350–1650 N provides a balance between cutting stability and deformation suppression. This work clarifies the coupled roles of the initial heat-treatment state and clamping constraints in thin-web gear finish turning and provides a reproducible modeling route for FRS and deformation prediction. Full article
(This article belongs to the Special Issue Metallic Functional Materials: Design, Processing, and Advanced Applications)
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21 pages, 4228 KB  
Article
Noise-Aware Machine Learning Accelerates Development of High-Latent-Heat Cu-Al-Ni Shape Memory Alloys for Thermal Management
by Donghua Zhou, Xiaohua Tian, Hongxing Li, Xiangyu Tong, Mingchao Zhang, Jieyu Meng, Yefei Wang, Wenbin Zhao, Jian Li and Changlong Tan
Materials 2026, 19(13), 2802; https://doi.org/10.3390/ma19132802 - 1 Jul 2026
Viewed by 439
Abstract
Cu-Al-Ni shape memory alloys (SMAs) are promising solid–solid phase-change materials (PCMs) for transient thermal management. Data-driven screening for high-latent-heat (ΔH) Cu-Al-Ni PCMs across the vast compositional space is efficient, but predictive accuracy and screening reliability degrade when noisy experimental data are [...] Read more.
Cu-Al-Ni shape memory alloys (SMAs) are promising solid–solid phase-change materials (PCMs) for transient thermal management. Data-driven screening for high-latent-heat (ΔH) Cu-Al-Ni PCMs across the vast compositional space is efficient, but predictive accuracy and screening reliability degrade when noisy experimental data are used. A noise-aware machine learning strategy was applied to accelerate the discovery of high-ΔH Cu-Al-Ni alloys with martensite start temperature (Ms) within the 100–200 °C range from noisy experimental datasets. The optimal noise level was estimated by minimizing the prediction error of the noise-aware Kriging model. The application of this strategy led to the discovery of four Cu-Al-Ni alloys with Ms ranging from 125 to 163 °C and ΔH ranging from 9.27 to 9.86 J/g. The best-performing Cu84Al13Ni3 (wt.%) alloy achieved Ms = 163 °C, ΔH = 9.86 J/g, thermal conductivity of 102 W·m−1·K−1 and figure of merit of 7272 × 106 J2 K−1 s−1 m−4. Its ΔH exceeds the previous highest Cu-Al-Ni ΔH in the 100–200 °C window by 11.8%, while its FOM exceeds the previous highest Cu-Al-Ni FOM by 33.75% and represents the highest value among the surveyed PCMs within the 100–200 °C range. After 100 thermal cycles, ΔH decreased by 0.158 J/g and Ms shifted by 0.9 °C, demonstrating good thermal cycling stability. Full article
(This article belongs to the Special Issue Metallic Functional Materials: Design, Processing, and Advanced Applications)
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