Jan Schroers

• Continuous thermoplastic drawing enables meter-scale BMG wires and tubes. • High-throughput fabrication achieved at strain rates of hundreds of s⁻¹. • Uniform wire diameters controlled precisely via draw ratio. • First scalable route for continuous hollow BMG tube fabrication. • Process preserves amorphous structure of Pd 35 Ni 45 P 17 B 3 and Zr 44 Ti 11 Ni 10 Cu 10 Be 25 alloys.

Seemingly identical Bulk Metallic Glasses (BMG) often exhibit strikingly different mechanical properties despite having the same composition and fictive temperature. A postulated mechanism underlying these differences is the presence of “defects” and density variations. Motivated by this perspective, we introduce physically realistic and quantitatively controllable density fluctuations in molecular dynamics simulations to systematically examine their role in shear band formation under applied stress. We find that the critical shear strain is strongly dependent on the magnitude and size of the fluctuations, revealing a nonlinear activation behavior associated with localized rejuvenation. This finding also elucidates why, historically, critical shear stresses obtained in simulations have differed so much from those found experimentally, as typical simulations setups might favor unrealistically uniform geometries.

General microstructures possess multiscale features (~10 -10 m – 10 -3 m) comprised of a vast number of atoms, and their properties cannot be directly reconstructed by the characteristics of their constituents. Therefore, a general quantitative understanding of microstructure-property-relationships remains elusive. To address this challenge, we propose local deformation mapping (LDM) as a characterization technique to determine mass transport characteristics. LDM is capable of probing microstructures with a resolution all the way down to ~50 nm 2 over macroscopic dimensions of ~cm 2 , resulting in ~10 12 simultaneous data points. This is experimentally achieved by pressing a nanomold with an array of nanopores against the microstructure, creating local stress gradients that force the material from the microstructure into the nanopores. The diffusing or slipping material from the microstructure fills these nanopores to form an array of nanorods on top of the microstructure. The nanorod array represents the microstructure’s local plastic response which is spatially separated from the underlying microstructure 1 . This separation enables sensitive kinetic and chemical characterization of this mass transport response. Collectively, these nanorods constitute a set of ~10 12 measurements forming the deformation response map generated in a single step. The local variations in a microstructure (due to the presence of the various microstructural features) which affect the local plastic deformation response are captured as the variation in the nanorod length L (x,y) map created via LDM. We then use the analytical models developed to transform these L maps to the local material transport properties map. Thus, we reveal spatial (or local) microstructure-property variations by mapping these transport properties and their variations onto the underlying microstructural features. Thus, these characteristics, such as diffusivity, and dislocation

Shear band stability is measured for a Zr-based bulk metallic glass in compressive bending and compared with previous results in tension. Compressive failure is induced via bending of trapezoidal cross-section beams. This characterization is done at multiple fictive temperatures and compared with results in tension, revealing a compression-tension asymmetry in shear banding stability. Stability, indicated here as the ability to resist catastrophic shear bands in favor of stable, arrested shear bands, is higher in compression than tension at all measured fictive temperatures. This asymmetry suggests that shear band propagation is different in tension and compression, and possible mechanisms underlying this difference are suggested. Additionally, this stability measurement is shown to be consistent in uniaxial compression testing, demonstrating its potential for predicting a brittle vs. ductile response in different loading modes of bulk metallic glasses.

Tailoring material properties often requires understanding the solidification process. Herein, we introduce the geometric descriptor Soliquidy, which numerically captures the Euclidean transport cost between the translationally disordered versus ordered states of a materials. As a testbed, we apply Soliquidy to the classification of glass-forming metal alloys. By extending and combining an experimental library of metallic thin films (glass/no-glass) with the aflow.org computational database (geometrical and energetic information of mixtures) we found that the combination of Soliquity and formation enthalpies generates an effective classifier for glass formation. Such a classifier is then used to tackle a public dataset of metallic glasses showing that the glass-agnostic assumptions of Soliquity can be useful for understanding kinetically-controlled phase transitions.

Tailoring material properties often requires understanding the solidification process. Herein, we introduce the geometric descriptor Soliquidy, which numerically captures the Euclidean transport cost between the translationally disordered versus ordered states of a materials. As a testbed, we apply Soliquidy to the classification of glass-forming metal alloys. By extending and combining an experimental library of metallic thin-films (glass/no-glass) with the aflow.org computational database (geometrical and energetic information of mixtures) we found that the combination of Soliquity and formation enthalpies generates an effective classifier for glass formation. Such classifier is then used to tackle a public dataset of metallic glasses showing that the glass-agnostic assumptions of Soliquity can be useful for understanding kinetically-controlled phase transitions.

Accurate prediction of the liquidus temperature ( T L ) of alloys remains a challenge despite numerous theoretical models. Here, we explore and analyze the degree to which machine learning, ML, strategies can be used to predict T L . We use established literature data on liquidus temperatures of 85,523 binary alloys to train ML models using various feature vectors to represent the alloys. While our results are comparable to previous studies, the persistent ∼8% error underscores the limitations of current ML models for practical usage. The suboptimal accuracy leads us to question how well-defined the problem is and to what degree fundamental limitations prevent us from attaining more accurate predictions. We identify two major challenges in predicting the liquidus temperature of binary alloys through supervised ML algorithms. One challenge is representing the relevant characteristics of an alloy that determines liquidus temperature through appropriate features. The other fundamental challenge is the discreteness of atomic properties. The difference between two elements and thereby alloy systems is significant, which makes it difficult to learn from one alloy system to predict properties of another. We argue that these problems can be reduced to some extent, however these challenges are common in complex materials science problems and constitute a fundamental challenge in applying supervised ML strategies in this context.

• Nano-patterned Pt-based bulk metallic glass (Pt-BMG) outperformed the flat and micro-patterned ones in hydrogen evolution. • Nano-patterned Pt-BMG demonstrates outstanding long-term stability and self-improving behavior in the stability test. • Surface characterizations confirmed a Cu x O foam formation on top of the nano-patterned surface during the stability test. • A three-step process is proposed to explain the Cu x O formation via dynamic hydrogen bubble templating electrodeposition. • The synergy of metallic glasses, thermoplastic patterning, and dynamic bubble templating provides an approach to synthesizing metal or metal-oxide foams. Hydrogen is a promising energy carrier for replacing fossil fuels, and hydrogen production via hydrogen evolution reaction (HER) is an environmentally friendly option if electrocatalysts with low overpotentials and long-term stability are used. In this work, the electrocatalytic performance of Pt 57.5 Cu 14.7 Ni 5.3 P 22.5 bulk metallic glass (BMG) with flat, micro-patterned, and nano-patterned surfaces for HER in 0.5 M H 2 SO 4 is studied. The nano-patterned Pt-BMG demonstrates outstanding long-term stability and self-improving behavior with a final overpotential of 150 mV and a Tafel slope of 42 mV dec −1 after 1000 linear sweep voltammetry (LSV) cycles, which is respectively 42 % and 37 % lower than in the first LSV cycle. X-ray photoelectron spectroscopy (XPS) and Auger electron spectroscopy (AES) indicate the formation of a layer of CuO/Cu 2 O foam deposited on top of the nano-patterned surface during the stability test of 1000 LSV cycles. A three-step process is proposed to explain the formation of Cu x O foam via dynamic hydrogen bubble templating (DHBT) electrodeposition from Cu dissolution of the Pt-BMG without using copper salt. This work provides a method to create Cu x O foams that could be used for various applications. Moreover, nano-patterned BMGs with DHBT deposition offer a feasible strategy to synthesize metal or metal-oxide foams.

Hydrogen is a promising energy carrier for replacing fossil fuels, and hydrogen production via hydrogen evolution reaction (HER) is an environmentally friendly option if electrocatalysts with low overpotentials and long-term stability are used. In this work, the electrocatalytic performance of $\mathrm{Pt_{57.5}Cu_{14.7}Ni_{5.3}P_{22.5}}$ bulk metallic glass (BMG) with flat, micro-patterned, and nano-patterned surfaces for HER in 0.5 M H2SO4 is studied. The nano-patterned Pt-BMG demonstrates outstanding long-term stability and self-improving behavior with a final overpotential of 150 mV and a Tafel slope of 42 $\mathrm{mV dec^{-1}}$ after 1000 linear sweep voltammetry (LSV) cycles, which is respectively 42% and 37% lower than in the first LSV cycle. X-ray photoelectron spectroscopy (XPS) and Auger electron spectroscopy (AES) indicate the formation of a layer of CuO/Cu2O foam deposited on top of the nano-patterned surface during the stability test of 1000 LSV cycles. A three-step process is proposed to explain the formation of CuxO foam via dynamic hydrogen bubble templating (DHBT) electrodeposition from Cu dissolution of the Pt-BMG without using copper salt. This work provides a method to create CuxO foams that could be used for various applications. Moreover, nano-patterned BMGs with DHBT deposition offer a feasible strategy to synthesize metal or metal-oxide foams.

The tolerances of alloys to impurities can vary significantly across impurity-alloy combinations and are largely unknown beyond the most common alloys and impurities. Further, a more general framework to quantify, compare, and practically utilize the tolerance of elements and alloys to impurities is missing. Here, we propose such a framework based on the parameter CIM, the maximum content of an impurity that can be added to a pure element before it no longer crystallizes and instead vitrifies, as measured under sputtering conditions. Using high throughput combinatorial methods, CIM can be readily determined for practically important impurity-element combinations. We argue that CIM generally indicates impurity tolerance because it ubiquitously measures solid solution stability and provide arguments on how conclusions may be drawn from impurity-element to impurity-alloy tolerance. This practical metric for evaluating impurity tolerances for alloys may help metallurgy by enabling greater recycled feedstock compatibility during manufacturing and, in the future, the design of more impurity tolerant alloys.

Abstract Ni‐free Ti‐based bulk metallic glasses (BMGs) are exciting materials for biomedical applications because of their outstanding biocompatibility and advantageous mechanical properties. The glassy nature of BMGs allows them to be shaped and patterned via thermoplastic forming (TPF). This work demonstrates the versatility of the TPF technique to create micro‐ and nano‐patterns and hierarchical structures on Ti 40 Zr 10 Cu 34 Pd 14 Sn 2 BMG. Particularly, a hierarchical structure fabricated by a two‐step TPF process integrates 400 nm hexagonal close‐packed protrusions on 2.5 µm square protuberances while preserving the advantageous mechanical properties from the as‐cast material state. The correlations between thermal history, structure, and mechanical properties are explored. Regarding biocompatibility, Ti 40 Zr 10 Cu 34 Pd 14 Sn 2 BMGs with four surface topographies (flat, micro‐patterned, nano‐patterned, and hierarchical‐structured surfaces) are investigated using Saos‐2 cell lines. Alamar Blue assay and live/dead analysis show that all tested surfaces have good cell proliferation and viability. Patterned surfaces are observed to promote the formation of longer filopodia on the edge of the cytoskeleton, leading to star‐shaped and dendritic cell morphologies compared with the flat surface. In addition to potential implant applications, TPF‐patterned Ti‐BMGs enable a high level of order and design flexibility on the surface topography, expanding the available toolbox for studying cell behavior on rigid and ordered surfaces.

Addressing complex materials science problems through machine learning (ML) is challenging. A primary reason for the challenge is that the underlying controlling mechanisms may vary within the considered problem space. To quantify this, we divide alloy data into subgroups and construct ML models to predict metallic glass formation. We discover that subgrouping guided by physical insights into the problem leads to significantly higher prediction accuracy. Specifically, when applying Inoue's subgrouping, models specific to subgroups outperform those trained on the entire dataset. Moreover, our analysis uncovers distinct mechanisms and contributions controlling the glass-forming ability in different subgroups, shedding light on the diverse nature of this phenomenon. Statistical methods for subgrouping prove less effective and constrained when compared to physics-informed subgrouping. Our results underscore the importance of leveraging physical insights for effective subgrouping or precise feature representation, to guide ML strategies when tackling complex materials science problems. Such an integrated approach has the potential to unlock new insights into material composition-property relationships and accelerate materials discovery in a wide range of applications beyond metallic glass formation.

The atomic-level structure of bulk metallic glasses is a key determinant of their properties. An accurate representation of amorphous systems in computational studies has traditionally required large supercells that are unfortunately computationally demanding to handle using the most accurate ab initio calculations. To address this, we propose to specifically design small-cell structures that best reproduce the local geometric descriptors (e.g., pairwise distances or bond angle distributions) of a large-cell simulation. We rely on molecular dynamics (MD) driven by empirical potentials to generate the target descriptors, while we use reverse Monte Carlo (RMC) methods to optimize the small-cell structure. The latter can then be used to determine mechanical and electronic properties using more accurate electronic structure calculations. The method is implemented in the Metallic Amorphous Structures Toolkit (MAST) software package.

The underlying atomistic mechanism of deformation is a central problem in mechanics and materials science. Whereas deformation of crystalline metals is fundamentally understood, the understanding of deformation of amorphous metals lacks behind, particularly identifying the involved temporal and spatial scales. Here, we reveal that at small scales the size-dependent deformation behavior of amorphous metals significantly deviates from homogeneous flow, exhibiting increasing deformation rate with reducing size and gradually shifted composition. This transition suggests the deformation mechanism changes from collective atomic transport by viscous flow to individual atomic transport through interface diffusion. The critical length scale of the transition is temperature dependent, exhibiting a maximum at the glass transition. While viscous flow does not discriminate among alloy constituents, diffusion does and the constituent element with higher diffusivity deforms faster. Our findings yield insights into nano-mechanics and glass physics and may suggest alternative processing methods to epitaxially grow metallic glasses.

The understanding and quantification of plasticity in crystalline metals, which has led to their widespread and effective usage as a structural material, is lacking in metallic glasses (MGs). Here, we introduce such a framework for plasticity. This very practical framework is based on a MGs’ ability to support stable shear band growth, quantified in a stress gradient, ∇σUS, which we measure and calculate for a range of MGs. Whether a MG deforms plastically prior to fracture or only elastic in an application is determined by the comparison between ∇σUS and the applied stress field gradient, ∇σapp; if ∇σUS>∇σapp, the MG can only deform elastic prior to fracture, if ∇σUS<∇σapp, the MG can plastically deform, and ∇σapp−∇σUS indicates the magnitude of plasticity. This framework can explain observed plastic properties of MGs and their apparent contradicting brittle and tough characteristics. Looking forward, proposed framework provides first MG specific constitutive relation to quantitatively model their plastic behavior in any application.

Abstract Reducing the sample size can profoundly impact properties of bulk metallic glasses. Here, we systematically reduce the length scale of Au and Pt-based metallic glasses and study their vitrification behavior and atomic mobility. For this purpose, we exploit fast scanning calorimetry (FSC) allowing to study glassy dynamics in an exceptionally wide range of cooling rates and frequencies. We show that the main α relaxation process remains size independent and bulk-like. In contrast, we observe pronounced size dependent vitrification kinetics in micrometer-sized glasses, which is more evident for the smallest samples and at low cooling rates, resulting in more than 40 K decrease in fictive temperature, T f , with respect to the bulk. We discuss the deep implications on how this outcome can be used to convey glasses to low energy states.

A coarse-grained spring network model is proposed for the prediction of the mechanical response of metallic glasses as a function of the microstructure prior to loading. This model describes the mechanical response of metallic glasses using a network of parallel springs that can break and reform, mimicking atomic rearrangements during deformation. We compare predictions of the spring network model for stress versus strain to results from numerical simulations of athermal quasistatic, uniaxial tensile deformation of ${\mathrm{Cu}}_{50}{\mathrm{Zr}}_{50}$ metallic glasses using Lennard-Jones (LJ) and embedded atom method (EAM) atomic interactions. We show that both the LJ and EAM models possess qualitatively similar stress $\ensuremath{\sigma}$ versus strain $\ensuremath{\gamma}$ curves. By specifying five parameters [ultimate strength, strain at ultimate strength, slopes of $\ensuremath{\sigma}(\ensuremath{\gamma})$ at $\ensuremath{\gamma}=0$ and at large strain, and strain at fracture where $\ensuremath{\sigma}=0]$, we demonstrate that the spring network model can accurately describe the form of the stress-strain curves during uniaxial tension for the computational studies of ${\mathrm{Cu}}_{50}{\mathrm{Zr}}_{50}$, as well as recent experimental studies of several Zr-based metallic glasses.

Metallic glasses are a new class of structural materials which exhibit exciting mechanical properties including high strength and elasticity. In terms of fracture toughness, the material class of metallic glasses spans a wide range; Some metallic glasses are extremely brittle and exhibit near ideal brittle behavior whereas others can be exceptional tough with values comparable to the toughest metals out there. Such large range of observed fracture toughness within the material class of metallic glasses is surprising as they have seemingly a very similar atomic structure. Therefore, we developed “artificial microstructures” which allows to decouple the various contribution of sample geometry, imperfection and structure. Specifically, we decouple variations in the alloys’ chemical composition and the atomic structure and quantified the resulting fracture toughness. Atomic structure of a metallic glass can be modified by the fictive temperature. The fictive temperature of a glass is the temperature at which the liquid metallic glass falls out of equilibrium upon colling and forms a glass. Upon further cooling the structure is maintained only thermal oscillations decrease due to a lower absolute temperature. We found that the effect of fictive temperature (same chemistry, different structural stages of the glass) is comparable to the variations of fracture toughness when the chemistry is varied. Hence, it appears that the subtle differences in the glass structure are responsible for the large range of fracture toughness’s observed. Our results reveal that fracture toughness within the material class of metallic glasses varies significantly and we found some example alloys with exceptional high resistance to fracture and others that are almost ideally brittle. Significant influences on the fracture toughness have the structure of the glass, its chemistry, and some imperfections in the structure.

The understanding and quantification of ductility in crystalline metals, which has led to their widespread and effective usage as a structural material, is lacking in metallic glasses (MGs). Here, we introduce such a framework for ductility. This very practical framework is based on a MGs ability to support stable shear band growth, quantified in a stress gradient, gradSDB, which we measure and calculate for a range of MGs. Whether a MG behaves ductile or brittle in an application is determined by the comparison between gradsDB the applied stress field gradient, gradsapp. If gradsDB &gt; gradsapp, the MG will behave brittle, if gradsDB &lt; gradsapp, the MG will behave ductile, and gradsapp - gradsDB indicates how ductile. This framework can explain observed plastic properties of MGs and their apparent contradicting brittle and ductile characteristics. Looking forward, proposed framework provides the constitutive relation to quantitatively model their plastic behavior in any application, a requirement to use MGs as structural materials.

The transition between necking-mediated tensile failure of glasses, at elevated temperatures and/or low strain rates, and shear-banding-mediated tensile failure, at low temperatures and/or high strain rates, is investigated using tensile experiments on metallic glasses and atomistic simulations. We experimentally and simulationally show that this transition occurs through a sequence of macroscopic failure patterns, parametrized by the ultimate tensile strength. Quantitatively analyzing the spatiotemporal dynamics preceding failure, using large scale atomistic simulations corroborated by experimental fractography, reveals how the collective evolution and mutual interaction of shear-driven plasticity and dilation-driven void formation (cavitation) control the various macroscopic failure modes. In particular, we find that, at global failure, the size of the largest cavity in the loading direction exhibits a nonmonotonic dependence on the temperature at a fixed strain rate, which is rationalized in terms of the interplay between shear- and dilation-driven plasticity. We also find that the size of the largest cavity scales with the cross-sectional area of the undeformed sample. Our results shed light on tensile failure of glasses and highlight the need to develop elastoplastic constitutive models of glasses incorporating both shear- and dilation-driven irreversible processes.

The atomic-level structure of bulk metallic glasses is a key determinant of their properties. An accurate representation of amorphous systems in computational studies has traditionally required large supercells that are unfortunately computationally demanding to handle using the most accurate ab initio calculations. To address this, we propose to specifically design small-cell structures that best reproduce the local geometric descriptors (e.g., pairwise distances or bond angle distributions) of a large-cell simulation. We rely on molecular dynamics (MD) driven by empirical potentials to generate the target descriptors, while we use reverse Monte Carlo (RMC) methods to optimize the small-cell structure. The latter can then be used to determine mechanical and electronic properties using more accurate electronic structure calculations. The method is implemented in the Metallic Amorphous Structures Toolkit (MAST) software package.

Abstract Development of an electrocatalyst that is cheap and has good properties to replace conventional noble metals is important for H 2 applications. In this study, dealloying of an amorphous Ti 37 Cu 60 Ru 3 alloy was performed to prepare a free‐standing nanostructured hydrogen evolution reaction (HER) catalyst. The effect of dealloying and addition of Ru to TiCu alloys on the microstructure and HER properties under alkaline conditions was investigated. 3 at.% Ru addition in Ti 40 Cu 60 decreases the overpotential to reach a current density of 10 mA cm −2 and Tafel slope of the dealloyed samples to 35 and 34 mV dec −1 . The improvement of electrocatalytic properties was attributed to the formation of a nanostructure and the modification of the electronic structure of the catalyst. First‐principles calculations based on density function theory indicate that Ru decreases the Gibbs free energy of water dissociation. This work presents a method to prepare an efficient electrocatalyst via dealloying of amorphous alloys.

Metallic glasses are frequently used as structural materials. Therefore, it is important to develop methods to predict their mechanical response as a function of the microstructure prior to loading. We develop a coarse-grained spring network model, which describes the mechanical response of metallic glasses using an equivalent series network of springs, which can break and re-form to mimic atomic rearrangements during deformation. To validate the model, we perform simulations of quasistatic, uniaxial tension of Lennard-Jones and embedded atom method (EAM) potentials for Cu$_{50}$Zr$_{50}$ metallic glasses. We consider samples prepared using a wide range of cooling rates and with different amounts of crystalline order. We show that both the Lennard-Jones and EAM models possess qualitatively similar stress $σ$ versus strain $γ$ curves. By specifying five parameters in the spring network model (ultimate strength, strain at ultimate strength, slopes of $σ(γ)$ at $γ=0$ and at large strain, and strain at fracture where $σ=0$), we can accurately describe the form of the stress-strain curves during uniaxial tension for the computational studies of Cu$_{50}$Zr$_{50}$, as well as recent experimental studies of several Zr-based metallic glasses. For the computational studies of Cu$_{50}$Zr$_{50}$, we find that the yield strain distribution is shifted to larger strains for slowly cooled glasses compared to rapidly cooled glasses. In addition, the average number of new springs and their rate of formation decreases with decreasing cooling rate. These effects offset each other at large strains, causing the stress-strain curve to become independent of the sample preparation protocol in this regime. In future studies, we will extract the parameters that define the spring network model directly from atomic rearrangements that occur during uniaxial deformation.