doi:10.1088/2752-5724/ac4558

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Recent development of chemically complex metallic glasses: from accelerated compositional design, additive manufacturing to novel applications

doi: 10.1088/2752-5724/ac4558

J Y Zhang¹,
Z Q Zhou¹,
Z B Zhang¹,
M H Park¹,
Q Yu¹,
Z Li²,
J Ma²,
A D Wang³,
H G Huang⁴,
M Song⁵,
B S Guo^{1, 6},
Q Wang⁷,
Y Yang^{1, 8, 9
,}

1.
Department of Mechanical Engineering, College of Engineering, City University of Hong Kong, Kowloon Tong, Kowloon, Hong Kong, People’s Republic of China
2.
Shenzhen Key Laboratory of High Performance Nontraditional Manufacturing, College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen, Guangdong 518060, People’s Republic of China
3.
School of Materials Science and Engineering, Dongguan University of Technology, Dongguan, Guangdong 523808, People’s Republic of China
4.
Institute of Materials, China Academy of Engineering Physics, Jiangyou, Sichuan 621907, People’s Republic of China
5.
State Key Laboratory of Powder Metallurgy, Central South University, Changsha, Hunan 410083, People’s Republic of China
6.
Institute of Advanced Wear & Corrosion Resistant and Functional Materials, Jinan University, Guangzhou, Guangdong 510632, People’s Republic of China
7.
Institute of Materials, Laboratory for Microstructures, Shanghai University, Shanghai 200444, People’s Republic of China
8.
Department of Materials Science and Engineering, College of Engineering, City University of Hong Kong, Kowloon Tong, Kowloon, Hong Kong, People’s Republic of China
9.
Department of Advanced Design and System Engineering, College of Engineering, City University of Hong Kong, Kowloon Tong, Kowloon, Hong Kong, People’s Republic of China

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Corresponding author: E-mail: yonyang@cityu.edu.hk

摘要

Abstract: Metallic glasses (MGs) or amorphous alloys are an important engineering material that has a history of research of about 80-90 years. While different fast cooling methods were developed for multi-component MGs between 1960s and 1980s, 1990s witnessed a surge of research interest in the development of bulk metallic glasses (BGMs). Since then, one central theme of research in the metallic-glass community has been compositional design that aims to search for MGs with a better glass forming ability, a larger size and/or more interesting properties, which can hence meet the demands from more important applications. In this review article, we focus on the recent development of chemically complex MGs, such as high entropy MGs, with new tools that were not available or mature yet until recently, such as the state-of-the-art additive manufacturing technologies, high throughput materials design techniques and the methods for big data analyses (e.g. machine learning and artificial intelligence). We also discuss the recent use of MGs in a variety of novel and important applications, from personal healthcare, electric energy transfer to nuclear energy that plays a pivotal role in the battle against global warming.
- metallic glasses /
- amorphous alloys /
- alloy design /
- additive manufacturing /
- machine learning /
- high throughput materials design
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Figure 1. Comparison of different classes of BMGs in terms of their percentage. Note that the comparison is based on a collection of 1000 BMGs with measured GFAs. The inset highlights the elements with counts indicating the total number of times an individual element being found in the reported MG compositions. Reproduced from [17]. CC BY 4.0.

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Figure 2. Combinatorial methods for MG synthesis and characterization. The variation of contrast in the heating and cooling cycle of Au-Cu-Si library section. Reprinted from [65], with the permission of AIP Publishing.

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Figure 3. The schematics for developing a machine learning model with classification and/or regression algorithms; (a)-(e) Reprinted from [17]. CC BY 4.0. (a) The MG database with thousands of compositions from established data sources or literature. (b) The development of data descriptors. (c) Classification algorithms used in the study of glass-forming likelihood. (d) Regression algorithms for the prediction of GFA. (e) Contour maps of predicted glass-forming likelihood diagram based on the prediction results of classification model; (f) The development of MGs with high GFA through the prediction of regression model. Reprinted from [71], Copyright (2020), with permission from Elsevier.

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Figure 4. The schematic diagrams for the AM techniques based on supercooled liquids and the photo of a typical AMed BMG sample. (a) and (b) spark plasma sintering (SPS); (a) was reprinted from [125]. CC BY 4.0. (b) Reprinted from [100], Copyright (2016), with permission from Elsevier; (c) and (d) friction joining/welding; (c) Reprinted from [29], Copyright (2004) with permission from Elsevier; (d) Reprinted from [105], Copyright (2004) with permission from Elsevier; (e) and (f) thermoplastic compression; (e) Reprinted from [111], Copyright (2020), with permission from Elsevier; (f) Reprinted from [33], Copyright (2003), with permission from Elsevier.

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Figure 5. (a) The schematic diagram and (b) the photo of printed BMG structures with selective laser melting (SLM). Figure (a) Reprinted from [24], Copyright (2021), with permission from Elsevier; (b) Reprinted from [112], Copyright (2017), with permission from Elsevier.

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Figure 6. (a) The schematic diagram to fabricate single- and multi-phase bulk sample by ultrasonic cold joining of the MG ribbons, (b) The MG interfaces activation and bonding mechanism under ultrasonic vibration, (c) Bulk composite sample of Zr-MG and HEA synthesized by ultrasonic joining, (d) The micro-sectional morphology of the HEA/MG composite, and the corresponding TEM diffraction images at different positions, (e) The shear bands formed in the MG matrix in the fracture surface of the composite under compression loading, (f) The strong embedding effect of HEA particles in the MG matrix observed in the fracture surface, (g) Stress-strain curve of HEA/MG composite obtained by ultrasonic additive manufacturing, comparing to that of the single HEA and MG, (a)-(g) adapted from [143] (2020) (© 2022 Springer Nature Switzerland AG. Part of Springer Nature.). With permission of Springer.

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Figure 7. (a) The schematic diagram of ultrasonic joining of BMGs; Reprinted from [150], Copyright (2020), with permission from Elsevier. (b) Various pairs of BMGs bonded by ultrasonic cold fusion; Pt-Pt-based MG (S1), Pt-La-based MG (S2), La-La-based MG (S3), Pd-Pd-based MG (S4), Zr-Zr-based MG1 (S5), and Zr-Zr-based MG2 (S6). (c)-(e) SEM image of bonding interface of La-La-based MG, Pt-La-based MG, and Pd-Pd-based MGs, respectively. (f)-(g) High-resolution TEM and SAED pattern of the bonding interface of Pt-La-based MG obtained by ultrasonic cold fusion. (h) Molecular dynamics simulation of atomic mobility of MGs showing that the activated energy required for the surface atoms is lower than that in the bulk; (b)-(h) Reprinted from [151], Copyright (2021) © 2022 Springer Nature Switzerland AG. Part of Springer Nature.). With permission of Springer.

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Figure 8. (a) A comparison of the magnetic entropy changes (|S_M|) under 5T and transition temperature (T_tran) among MGs and crystalline magnetic refrigerants [158, 159]. (b) Summary of T_g versus liquidus temperature of various MGs; Reproduced from [17]. CC BY 4.0.

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Figure 9. The photograph of U-based glass matrix composites; Reprinted from [174], Copyright (2018), with permission from Elsevier.

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Figure 10. (a) Dependences of GFA and B_s on the ferromagnetic element (Fe(Co, Ni)) content in different alloy systems, showing the GFA and B_s limits in the Fe-based metallic glass. (b) Atomic number and radius of the component elements in Fe(Co, Ni)SiBPC alloys (inset shows the mixing enthalpy). (c) Other influences of Co/Ni alloying; (a)-(c) [200] (2021) © 2022 Springer Nature Switzerland AG. Part of Springer Nature.). With permission of Springer. (d) Fe content of the electric steels and commercial MGs [198]. (e) Image of annealed Fe_85.7Si_2.3B_9.7P_1.5C_0.8 ribbon after bending for 180. (f) SEM image of the shear zone in the bending region; (d)-(f) [198] (2017) © 2022 Springer Nature Switzerland AG. Part of Springer Nature.). With permission of Springer.

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Figure 11. (a) Tafel slope versus the overpotential at 10 mA cm^-2 for the Ir₂₅Ni₃₃Ta₄₂ MG film or HER in 0.5 _M H₂SO₄ and other existing MG catalysts for HER. (b) turnover frequency (TOF) values averaged over all surface sites of the Ir₂₅Ni₃₃Ta₄₂ MG film compared with the Ir film, Pt film, and other highly active HER catalysts, including molybdenum sulfide-based catalysts, transition metal phosphides, and precious-metal-containing catalysts; [208] John Wiley & Sons. [© 2019 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim].

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Figure 12. (a) A comparison plot of ductility versus yield strength of various coating materials; Reprinted from [218], Copyright (2020), with permission from Elsevier. (b) The wound healing after seven days for hairless skin grafting; Reprinted from [221], Copyright (2019), with permission from Elsevier.

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Figure 13. (a) The prices of elements in forming MGs. (b) A material cost comparison between different types of BMGs and conventional alloys.

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Table 1. The up-to-date reported BMG/MG compositions discovered by machine learning.

Composition	Year	Data descriptors	Algorithms	D_max (mm)	References
Zr_59.2Cu_16.2Ni_12.6Al_9.6Hf_2.2Ti_0.2	2021	8 descriptors based on physical models	Adaptive boosting, GPR, ANN, SVM, RF	5	[17]
Zr₆₅Cu_17.5Ni₁₀Al_7.5	2018	213 descriptors based on empirical rules	RF	4.4	[76]
Zr₆₀Cu_17.5Ni₁₀Al_7.5Ti₅	2018	213 descriptors based on empirical rules	RF	4.2	[76]
Zr₅₅Cu₂₃Al_12.5Ni_7.5Ti₂	2018	213 descriptors based on empirical rules	RF	3.8	[76]
Zr₄₇Cu_23.5Al₁₅Ni_11.5Ti₃	2018	213 descriptors based on empirical rules	RF	3.1	[76]
Zr₅₅Cu₂₀Al₁₅Co₁₀	2021	8 descriptors based on physical models	Adaptive boosting, GPR, ANN, SVM, RF	3	[17]
Zr₅₄Ni₁₆Cu₁₄Ti₁₀Al₆	2021	8 descriptors based on physical models	Adaptive boosting, GPR, ANN, SVM, RF	3	[17]
Zr₅₅Cu₂₀Co₁₀Ti₈Al₇	2021	8 descriptors based on physical models	Adaptive boosting, GPR, ANN, SVM, RF	3	[17]
Zr₅₁Co₃₉Al₁₀	2021	52 descriptors based on chemical compositions	Correlation-based neural network	3	[87]
(Zr₆₂Co₃₂Al₆)₉₅Si₅	2021	52 descriptors based on chemical compositions	Correlation-based neural network	3	[87]
(Zr₆₃Co₂₄Al₁₃)₉₅Ni₅	2021	52 descriptors based on chemical compositions	Correlation-based neural network	3	[87]
Zr₄₇Cu₂₃Ni₁₈Al₁₀Ti₂	2018	213 descriptors based on empirical rules	RF	2.4	[76]
Zr₄₈Cu_29.5Al₁₈Ni_4.5	2018	213 descriptors based on empirical rules	RF	2.2	[76]
Zr₅₅Cu₂₀Ti₁₀Co₁₀Al₅	2021	8 descriptors based on physical models	Adaptive boosting, GPR, ANN, SVM, RF	2	[17]
Zr₄₅Cu₂₀Ti₁₀Al₁₀Co₁₀Hf₅	2021	8 descriptors based on physical models	Adaptive boosting, GPR, ANN, SVM, RF	2	[17]
(Zr₅₈Co₁₁Al₃₁)₉₅W₅	2021	52 descriptors based on chemical compositions	Correlation-based neural network	2	[87]
ZrHfAlCo	2021	8 descriptors based on physical models	Adaptive boosting, GPR, ANN, SVM, RF	0.05	[17]
ZrHfAlCoCu	2021	8 descriptors based on physical models	Adaptive boosting, GPR, ANN, SVM, RF	0.05	[17]
ZrHfAlCoNiCu	2021	8 descriptors based on physical models	Adaptive boosting, GPR, ANN, SVM, RF	0.05	[17]
TiZrHfCo	2021	8 descriptors based on physical models	Adaptive boosting, GPR, ANN, SVM, RF	0.05	[17]
TiZrHfAlCo	2021	8 descriptors based on physical models	Adaptive boosting, GPR, ANN, SVM, RF	0.05	[17]
TiZrHfAlCoCu	2021	8 descriptors based on physical models	Adaptive boosting, GPR, ANN, SVM, RF	0.05	[17]

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