Bulk Metallic Glasses XX: Atomistic Simulations, Modelling and Theory
Sponsored by: TMS Structural Materials Division, TMS: Mechanical Behavior of Materials Committee
Program Organizers: Robert Maass, Federal Institute of Materials Research and Testing (BAM); Peter Derlet, Paul Scherrer Institut; Katharine Flores, Washington University in St. Louis; Yonghao Sun, The Chinese Academy of Sciences; Lindsay Greer, University of Cambridge; Peter Liaw, University of Tennessee
Wednesday 8:30 AM
March 22, 2023
Room: Aqua C
Location: Hilton
Session Chair: Thomas Hardin, Sandia National Laboratories
8:30 AM Invited
Elucidating the Structure of Glass: Bottom-up or Top-down?: Takeshi Egami1; 1University of Tennessee
The conventional approach to elucidate the structure of glass and liquid is to start with a local cluster of several atoms and build up the system by adding more atoms. This approach, however, cannot explain why good medium-range order (MRO) is observed even in complex glasses. We propose to add a top-down approach, in which we start with a high-density gas state and introduce interatomic potential in reciprocal space. We use the pseudopotential without strong repulsion because no pair of atoms come that close to each other for repulsion to contribute to the total energy. The pseudopotential induces density waves and drives the system to the structurally coherent ideal glass state. But the two approaches, local bottom-up and global top-down, are incompatible, and compromise results in the MRO. This balanced approach explains various properties of liquid and glass. This work is supported by the US Department of Energy.
8:50 AM
Emergent Structural and Temporal Length Scales in Metallic Glasses - An Atomistic Simulation Perspective: Peter Derlet1; Robert Maass2; 1Paul Scherrer Institut; 2Federal Institute of Materials Research and Testing (BAM)
Metallic glasses contain emergent structural heterogenieties which are closely related to spatial variations in relaxation time-scales. These already manifest themselves in the under-cooled liquid regime above the glass transition temperature regime, and at lower temperatures are connected to the degree of relaxation of the glassy structure. Atomistic simulations of a model binary glass system spanning timescales of up to several microseconds will be presented, revealing not only the microscopic origin and nature of these heterogenities, but also how they influence dissipation, transport and ultimately the mechanical properties of the model glass.
9:10 AM
The Role of Structural Motifs and Outliers in the Deformation of Metallic Glasses: Porter Weeks1; Suzanne Russo1; Katharine Flores1; 1Washington University in St Louis
Machine learning-based clustering algorithms have recently been used to characterize the short-range order in disordered binary and ternary metallic systems. In contrast to other structural analyses (e.g. Voronoi), density-based clustering results in a surprisingly small number of characteristic structural motifs; only ~6-8 motifs are required to describe more than 95% of the atoms in the system. Variations in the population of these motifs with composition have been shown to be an excellent indicator of glass forming ability. This raises questions about the role of the characteristic motifs and outlier atoms in other properties, particularly mechanical deformation. Here, we investigate correlations between the populations of clusters in simulated Cu-Zr and Cu-Zr-Al glasses and their experimentally-determined mechanical properties, including nanoindentation modulus, hardness, and plastic work ratio. We then perform molecular dynamics simulations of selected glass compositions to investigate the evolution of clusters and outlier atoms during plastic deformation.
9:30 AM
Glass Formation and Shear Banding in CrMnFeCoNi High-entropy Metallic Glasses: A Molecular Dynamics Study: Marie Charrier1; Daniel Utt1; Arne Klomp1; Karsten Albe1; 1TU Darmstadt
Metallic glasses are strong but brittle, whereas high-entropy alloys show remarkable ductility. In this work, we use atomistic computer simulations to study the combination of these two materials classes. We utilize high quench rates to kinetically suppress crystallization in the Cantor (CoCrFeMnNi) high-entropy alloy to obtain a metallic glass. Our findings reveal that the thermodynamic state of the glass depends on the chemical and structural short- / medium-range order. This is critically affected by quench rate. Slowly quenched and, thus, well-relaxed glasses exhibit a higher number of preferred structural motifs compared to a poorly-relaxed glass. In glasses with high structural ordering, the failure mechanism switches from metal-like to brittle during mechanical testing. The yield strength and stiffness increase and the glass deforms via shear localization. Compared to a Cantor nanocrystal, a well-relaxed glass shows superior strength but reduced stiffness.
9:50 AM
Origin of Low Temperature Mechanical Loss in Metallic Glass: Leo Zella1; Jaeyun Moon2; Takeshi Egami1; 1University Tennessee Knoxville; 2Oak Ridge National Laboratory
Mechanical loss in metallic glass is important for the mechanical properties, ductility, internal friction, and brittleness of metallic glass. However, the microscopic mechanism of mechanical loss is not well understood. Using molecular dynamics (MD) simulations of sinusoidal mechanical perturbation, we have studied the low temperature mechanical loss in a prototypical Cu64.5 Zr35.5 metallic glass to identify the mechanism and nature of mechanical loss. Using dynamical mechanical spectroscopy (DMS) with atomic-level resolved stresses in MD, we have identified different groups of atoms involved in the mechanical loss based on their atomic-level viscoelastic response. We show the cooperative nature of the lossy atoms, which groups contribute the most to the mechanical loss, their spatial aggregations and the atomic shear stress build up that results in the mechanical loss.
10:10 AM Break
10:30 AM Invited
Metallic Glasses' Global Energy and Structural Heterogeneity Predicted by Machine Learning: Yuchu Wang1; Yue Fan1; 1University of Michigan
A new ML pipeline is developed to study the structures and properties of amorphous solids. We employ SOAP descriptor to encode the local environments, which are then fed into extreme gradient boosting tree algorithm to train, learn, and eventually predict the global configurational energy of metallic glasses. We identify 40 important unique local environments (ULEs) that are most responsible for the energy of a given glass sample. A designed decoding stage is then employed to decompose a sample’s 3N degrees of freedom configuration into a 40-dimension probability vector via frequency mapping of those ULEs. The obtained probability spectrum barcode is thus regarded as a signature representation of an interested sample. We further demonstrate that, through the analysis of occupational factions and fluctuations of the barcode, one can simultaneously estimate a sample’s global energy and level of structural heterogeneity. The physical interpretations of these ULEs and their implications are also discussed.
10:50 AM
Machine Learning versus Human Learning in Complex Materials Discovery and Science: Predicting Glass-forming Ability of Metallic Glasses: Guannan Liu1; Sungwoo Sohn1; Sebastian Kube1; Arindam Raj1; Andrew Mertz1; Anna Gilbert1; Mark Shattuck1; Corey O’Hern1; Jan Schroers1; 1Yale University
Glass formation is defined by a large number of atoms, thus often too complex to be solved using first-principles calculations. Machine learning (ML) appears to be able to overcome the limitations of today's approach to such complex materials. To test ML’s ability, we created ML models that predict glass-forming ability. Surprisingly, we discovered that when prediction accuracy is tested using interpolation, a general-material ML model with features constructed using simple statistical functions from elemental features is indistinguishable from models that use unphysical features or do not consider any features. Only when significant separation of training and testing data is performed, the general-material model outperforms the unphysical or composition models, but performs significantly worse than a human learning-based model. The general-material model's limited performance is explained by its inability to accurately represent alloy features through elemental features. We conclude that complex materials problems necessitate physical insights to develop effective ML models.
11:10 AM
Development and Application of an Atomic Cluster Expansion Potential for the CuZr System: Niklas Leimeroth1; Karsten Albe1; Jochen Rohrer1; 1TU Darmstadt
Molecular dynamics simulations play a key role in understanding atomistic mechanisms in bulk metallic glasses, but were limited by the accuracy of employed classic interatomic potentials. Novel machine learning potentials allow for near DFT accuracy simulations over a wide configuration space, enabling a precise description of crystalline and amorphous structures as well as interfaces. Therefore, we developed an atomic cluster expansion potential for the prototypical CuZr metallic glass system that accurately reproduces the energy landscape of the system over the whole compositional range. We applied our improved potential to investigate the amorphization of crystalline nanolayers between amorphous phases and the mechanical properties of glasses with precipitates. Additionally we tested the accuracy of the potential regarding thermodynamic properties by calculating a phase diagram for the system using the calphy code.
11:30 AM
Quantifying the Local Structure of Metallic Glass as a Function of Composition, Atomic Size, and Processing History: Thomas Hardin1; Michael Chandross1; Murray Daw2; 1Sandia National Laboratories; 2Clemson University
We report a series of molecular dynamics simulations using the EAM-X potential (which allows us to model metallic alloys with an easy-to-change range of atomic size ratios and energetic parameters) to sample the design space of binary metallic glasses, varying composition, atomic size ratio, and thermomechanical processing history. We used data mining techniques (the Gaussian Integral Inner Product Distance with agglomerative clustering and diffusion maps) to map out the local structural states of the glass as a function of these variables. This analysis enables the development of a science basis for rule-of-thumb relationships between composition, atomic size, processing history, and local structure. This presentation additionally looks forward to synergistic use of atomistic data mining, existing glassy structural descriptors, and emerging characterization techniques to enhance the impact of local structure in metallic glass research. SNL is managed and operated by NTESS under DOE NNSA contract DE-NA0003525 (SAND2022-8662 A).