Recent Advances in Metallic Glasses

TL;DR

This paper surveys recent advances in metallic glasses across experimental, additive manufacturing, and modeling fronts. It highlights how new experimental tools and in situ techniques illuminate the links between local structure (SRO/MRO) and mechanical response, while LPBF and other AM methods expand the manufacturability of MGs and introduce novel microstructures. It synthesizes nanoscale to continuum modeling, including ML-informed interatomic potentials, data-driven plasticity, and coarse-graining strategies that bridge scales, to improve design and predictive capabilities. The authors identify key challenges—controlling processing histories, exploiting GFA, mitigating defects in AM, and developing robust multiscale models—and underline the potential of data-driven approaches to accelerate discovery and optimization in MGs. Overall, the work maps a path toward reliable, scalable MG components with tailored mechanical properties for advanced applications.

Abstract

This paper reviews recent advances in the field of metallic glasses, focusing on the development of novel experimental techniques and in silico models. We discuss progress in experimental characterization, additive manufacturing, multiscale modeling approaches, and the growing role of machine learning in understanding and designing these complex materials. On the experimental side, we highlight measurements of thermophysical properties of supercooled liquids via fast chip calorimetry and enhancements in mechanical properties through rejuvenation treatments. This work underscores the crucial role of short-range order and medium-range order in controlling metallic glass mechanical properties. Recent progress in structural probes allows in situ observations of deformation mechanisms, positioning the field well to further advance our understanding of mechanical properties. Additive manufacturing of metallic glasses is discussed as one encouraging new manufacturing route for metallic glasses. We examine laser powder-bed fusion process physics and the central trade-off between amorphicity and densification, including heat affected zone devitrification and defects formation, together with emerging mitigation strategies and applications. On the theoretical and simulation side, we review advances in nanoscale, mesoscale, and continuum modeling of metallic glasses that have led to promising approaches by which multiscale schemes can incorporate data sourced from atomic-scale simulations. These efforts have helped to elucidate the connection between the glass structure and mechanical and rheological responses. We also cover the development of machine learning interatomic potentials for metallic glasses, along with machine learning driven prediction of glass forming ability and inverse design methods. Finally, challenges and directions for future research are presented and discussed.

Figures (12)

• Figure 1: Challenges of metallic glasses. a) Glass Forming Ability: Schematic time-temperature-transformation diagram showing the critical cooling rate to bypass crystallization. Fast, intermediate, and slow cooling regimes correspond to cooling rates $R_c\sim10^{5}$--$10^{6}$ K/s (1960s), $R_c\sim10^{2}$ K/s (1970s--1980s), and $R_c\sim1$ K/s (1990s), respectively. SCL denotes the supercooled liquid region. The underlying conceptual framework is discussed in Ref. gallino_busch_book. b) Thermomechanical History: Schematic illustrating various possibilities for thermomechanically relaxing or rejuvenating metallic glasses. Points a, b, and c represent possible initial energy states for a glass that may evolve to higher or lower energy after exposure to mechanical deformation and/or elevated temperatures. Figure reproduced from Ref. sun2016thermomechanical with permission from Springer-Nature. c) Brittleness and Ductility: An Ashby plot of yield strength versus fracture toughness showing the wide range of fracture toughness values that have been achieved for metallic glasses with relatively good fracture toughness based on Pd, Zr, or Ni. Figure reproduced from Ref. Li_JALCOM2025 with open access CC BY license. d) Local structure and its influence on properties: 3D atomic packing of face centered cubic like (at left) and hexagonal close packed like (at right) medium range order in a metallic glass sample revealed by atomic electron tomography. Solute centers are shown as large red spheres with solvent atoms shown in blue and green. Figures reproduced from Ref. Yang_Nature2021 with permission from Springer-Nature.
• Figure 2: Multiscale characterization of metallic glasses. Overview of metallic glasses (MGs) from atomic to macroscopic dimensions. From left to right: a) Atomic scale representation of a ZrCuAl MGs makinen2024bayesian from molecular dynamics simulations. b) Schematic of machine learning predictions for MG trajectories, where $t$ and $t'$ represent two different times. c) Schematic representation of elastoplastic models, in which each lattice site represents a coarse-grained region containing a group of particles. d) Cross-section electron microscopy image of a MG thin film produced with vapor deposition, showing nanostructured morphology, from Ref. brognara2022effect. e) Scanning electron micrographs of micron-sized gas atomized MG powders, from Ref. Bosong_Li2024AM. f) Example of bulk MG samples from Ref. schroers2010processing. g) Soft-magnetic additive manufactured MG (via laser podwer bed fusion) with internal complex geometries THORSSON2022110483 (metamaterials). At the bottom: Popular modeling approaches (beige) and manufacturing methods (light blue), are arranged according to the length scales at which they operate. Figures reproduced with permissions.
• Figure 3: Examples of thermophysical properties measured in bulk metallic glasses. a) Detection of a time temperature transformation (TTT) diagram around the nose for crystallization is enabled by chip-calorimetry (FDSC, open squares) complementing standard calorimetry experiments (DSC, pentagons and triangles) which only cover longer times. The combinations of these calorimetric techniques opens the possibility of disentangling homogeneous and heterogeneous nucleation effects using appropriate models (yellow and green dashed lines). Figure reproduced from Ref. frey2022 with open access CC BY license. b) Activation plot of a Au-based BMG showing $\alpha$-relaxation time data obtained by X-ray photon correlation spectroscopy (XPCS) and dynamic mechanical analyzer (DMA) and vitrification kinetics obtained using various calorimetric approaches (DSC, FSC). The $\alpha$-relaxation and the vitrification kinetics show a clear decoupling which becomes stronger at lower temperatures. This decoupling is also dependent on the cooling rate (open diamonds). The dashed lines are fits to the data using the Vogel-Fulcher-Tammann expression, and the dotted lines are fits using the Arrhenius ansatz. Figure reproduced with permissions from Ref. monnier2020. c) Normalized enthalpy relaxation during physical aging at different temperatures, showing hierarchical decays. Figure reproduced with permissions from Ref. gallino2018.
• Figure 4: a) Two-times autocorrelation functions measured by XPCS during low temperature aging of a BMG showing highly heterogeneous intermittent aging dynamics indicative of a complex energy landscape. Figure reproduced with permission from Ref. evenson2015. b) Two-times autocorrelation function showing heterogeneous aging behavior consisting of periods of stationary dynamics (labeled a and b) interconnected with fast-motion events. Figure reproduced with permission from Re. gallino2018. c) Fragility plot of viscosity versus the $T_g$-scaled inverse temperature for 14 metallic glass-formers, in comparison to SiO$_2$ and o-terphenyl. The solid lines are the fits of equilibrium viscosity data to the VFT-equation. Figure reproduced under CC-BY licence from Ref. gallino2017fragility. d) Temperature dependence of the relaxation time $\tau$ measured by XPCS (circles) and DMA (triangles) in the Au$_{49}$Cu$_{26.9}$Si$_{16.3}$Ag$_{5.5}$Pd$_{2.3}$ metallic glass former. The change in the slope of the experimental data is associated to a liquid-liquid transition (highlighted in grey) between two liquids of different fragility. The inset is a zoom of the transition range. e) The same LLT is measured by high-intensity X-ray diffraction applying the same thermal protocol used for the XPCS analysis in panel d) (blue diamonds). The observable reported on the y-axis as a function of the temperature, $[Q_p(T_{ref})/Q_p(T)]^3$, is the inverse of the cube of the position of the first sharp diffraction peak of the static structure factor normalized to that measured at $T_{ref}$=395.5 K, and is a proxy of the volume. Note that this observable depends on the cooling rate: during continuous cooling with 1.5 K/min (green triangles), the LLT is no longer visible. The grey dashed line is the standard behavior in absence of the LLT as shown by the green triangles. Figures reproduced from Refs. hechler2018 with permissions.
• Figure 5: a) Linear correlation between medium range order (MRO) cluster size and local hardness measured for two different BMG compositions. The correlation between hardness and MRO was maintained even after the hardness was altered by cryogenic thermal cycling, high pressure torsion, or cold rolling and a similar linear correlation was found between hardness and MRO volume fraction. Inset shows a schematic of an MRO cluster acting as a nucleus for a shear transformation zone. Figure reprinted from Ref. Nomoto_MaterTod2021 with permission from Elsevier. b)-e) Direct observations of shear band birth, propagation, and arrest visualized by the von Mises strains. From b) to c) the progressive extension of shear band I is observed with increasing strain along with the nucleation and arrest of shear band II while d) and e) give a higher magnification view of the regions within the black rectangles. Figure reproduced from Ref. Glushko_NatComm2024 with open access CC BY license.