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Register a new userSpectral descriptors for bulk metallic glasses based on the thermodynamics of competing crystalline phases
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Metallic glasses have attracted considerable interest in recent years due to their unique combination of superb properties and processability. Predicting bulk metallic glass formers from known parameters remains a challenge and the search for new systems is still performed by trial and error. It has been speculated that some sort of confusion during crystallization of the crystalline phases competing with glass formation could play a key role. Here, we propose a heuristic descriptor quantifying confusion and demonstrate its validity by detailed experiments on two well-known glass forming alloy systems. With the insight provided by these results, we develop a robust model for predicting glass formation ability based on the spectral decomposition of geometrical and energetic features of crystalline phases calculated ab-initio in the AFLOW high throughput framework. Our findings indicate that the formation of metallic glass phases could be a much more common phenomenon than currently estimated, with more than 17% of binary alloy systems being potential glass formers. Our approach is capable of pinpointing favorable compositions, overcoming a major bottleneck hindering the discovery of new materials. Hence, it is demonstrated that smart descriptors, based solely on the energetics and structure of competing crystalline phases calculated from first-principles and available in online databases, others the sought-after key for accelerated discovery of novel metallic glasses.
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The bulk and surface dynamics of Cu50Zr50 metallic glass were studied using classical molecular dynamics (MD) simulations. As the alloy undergoes cooling, it passes through liquid, supercooled, and glassy states. While bulk dynamics showed a marked slowing down prior to glass formation, with increasing activation energy, the slowdown in surface dynamics was relatively subtle. The surface exhibited a lower glass transition temperature than the bulk, and the dynamics preceding the transition were accurately described by a temperature-independent activation energy. Surface dynamics were much faster than bulk at a given temperature in the supercooled state, but surface and bulk dynamics were found to be very similar when compared at their respective glass transition temperatures. The manifestation of dynamical heterogeneity, as characterized by the non-Gaussian parameter and breakdown of the Stokes-Einstein equation, was also similar between bulk and surface for temperatures scaled by their respective glass transition temperatures. Individual atom motion was dominated by a cage and jump mechanism in the glassy state for both the bulk and surface. We utilize this cage and jump mechanisms to separate the activation energy for diffusion into two parts: (i) cage-breaking barrier (Q1), associated with the rearrangement of neighboring atoms to free up space and (ii) the subsequent jump barrier (Q2). It was observed that Q1 dominates Q2 for both bulk and surface diffusion, and the difference in activation energies for bulk and surface diffusion mainly arose from the differences in cage-breaking barrier Q1.
Mechanical behaviors of bulk metallic glasses (BMGs) including heterogeneous and homogeneous deformation are interpreted by phenomenological shear transformation zones (STZs) model. Currently, information about STZs, i.e. size and density, is only extracted by fitting model equation to the data obtained from macroscopic mechanical tests. This is inadequate since structural features of STZs theory cannot be assessed. Here, we develop anisotropic pair distribution function (PDF) method for directly characterizing mechanical response of deformation defects. Our results reveal the physical picture of deformation defects in BMGs and also provide direct experimental observation of a link between mechanical deformation and intrinsic properties of deformation defects in BMGs.
Inelastic deformation of metallic glasses occurs via slip events with avalanche dynamics similar to those of earthquakes. For the first time in these materials, measurements have been obtained with sufficiently high temporal resolution to extract both the exponents and the scaling functions that describe the nature, statistics and dynamics of the slips according to a simple mean-field model. These slips originate from localized deformation in shear bands. The mean-field model describes the slip process as an avalanche of rearrangements of atoms in shear transformation zones (STZs). Small slips show the predicted power-law scaling and correspond to limited propagation of a shear front, while large slips are associated with uniform shear on unconstrained shear bands. The agreement between the model and data across multiple independent measures of slip statistics and dynamics provides compelling evidence for slip avalanches of STZs as the elementary mechanism of inhomogeneous deformation in metallic glasses.
Metallic glasses are excellent candidates for biomedical implant applications due to their inherent strength and corrosion resistance. Use of metallic glasses in structural applications is limited, however, because bulk dimensions are challenging to achieve. Glass-forming ability (GFA) varies strongly with alloy composition and becomes more difficult to predict as the number of chemical species in a system increases. Here we present a theoretical model - implemented in the AFLOW framework - for predicting GFA based on the competition between crystalline phases, and apply it to biologically relevant binary and ternary systems. Elastic properties are estimated based on the rule of mixtures for alloy systems that are predicted to be bulk glass-formers. Focusing on Ca- and Mg-based systems for use in biodegradable orthopedic support applications, we suggest alloys in the AgCaMg and AgMgZn families for further study; and alloys based on the compositions: Ag$_{0.33}$Mg$_{0.67}$, Cu$_{0.5}$Mg$_{0.5}$, Cu$_{0.37}$Mg$_{0.63}$ and Cu$_{0.25}$Mg$_{0.5}$Zn$_{0.25}$.
Molecular dynamics simulations using an interatomic potential developed by artificial neural network deep machine learning are performed to study the local structural order in Al90Tb10 metallic glass. We show that more than 80% of the Tb-centered clusters in Al90Tb10 glass have short-range order (SRO) with their 17 first coordination shell atoms stacked in a 3661 or 15551 sequence. Medium-range order (MRO) in Bergman-type packing extended out to the second and third coordination shells is also clearly observed. Analysis of the network formed by the 3661 and 15551 clusters show that ~82% of such SRO units share their faces or vertexes, while only ~6% of neighboring SRO pairs are interpenetrating. Such a network topology is consistent with the Bergman-type MRO around the Tb-centers. Moreover, crystal structure searches using genetic algorithm and the neural network interatomic potential reveal several low-energy metastable crystalline structures in the composition range close to Al90Tb10. Some of these crystalline structures have the 3661 SRO while others have the 15551 SRO. While the crystalline structures with the 3661 SRO also exhibit the MRO very similar to that observed in the glass, the ones with the 15551 SRO have very different atomic packing in the second and third shells around the Tb centers from that of the Bergman-type MRO observed in the glassy phase.
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