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Register a new userNanoscratching of metallic glasses -- An atomistic study
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Tribological properties of materials play an important role in engineering applications. Up to now, a number of experimental studies have identified correlations between tribological parameters and the mechanical response. Using molecular dynamics simulations, we study abrasive wear behavior via nanoscratching of a Cu$_{64.5}$Zr$_{35.5}$ metallic glass. The evolution of the normal and transverse forces and hardness values follows the behavior well known for crystalline substrates. In particular, the generation of the frontal pileup weakens the response of the material to the scratching tip and leads to a decrease of the transverse hardness as compared to the normal hardness. However, metallic glasses soften with increasing temperature, particularly above the glass transition temperature thus showing a higher tendency to structurally relax an applied stress. This plastic response is analyzed focusing on local regions of atoms which underwent strong von-Mises strains, since these are the basis of shear-transformation zones and shear bands. The volume occupied by these atoms increases with temperature, but large increases are only observed above the glass transition temperature. We quantify the generation of plasticity by the concept of plastic efficiency, which relates the generation of plastic volume inside the sample with the formation of external damage, viz. the scratch groove. In comparison to nanoindentation, the generation rate of the plastic volume during nanoscratching is significantly temperature dependent making the glass inside more damage-tolerant at lower temperature but more damage-susceptible at elevated temperatures.
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Using molecular dynamics simulation, we study the plastic zone created during nanoindentation of a large CuZr glass system. The plastic zone consists of a core region, in which virtually every atom undergoes plastic rearrangement, and a tail, where the density distribution of the plastically active atoms decays to zero. Compared to crystalline substrates, the plastic zone in metallic glasses is significantly smaller than in crystals. The so-called plastic-zone size factor, which relates the radius of the plastic zone to the contact radius of the indenter with the substrate, assumes values around 1, while in crystals -- depending on the crystal structure -- values of 2--3 are common. The small plastic zone in metallic glasses is caused by the essentially homogeneous deformation in the amorphous matrix, while in crystals heterogeneous dislocations prevail, whose growth leads to a marked extension of the plastic zone.
The atomic theory of elasticity of amorphous solids, based on the nonaffine response formalism, is extended into the nonlinear stress-strain regime by coupling with the underlying irreversible many-body dynamics. The latter is implemented in compact analytical form using a qualitative method for the many-body Smoluchowski equation. The resulting nonlinear stress-strain (constitutive) relation is very simple, with few fitting parameters, yet contains all the microscopic physics. The theory is successfully tested against experimental data on metallic glasses, and it is able to reproduce the ubiquitous feature of stress-strain overshoot upon varying temperature and shear rate. A clear atomic-level interpretation is provided for the stress overshoot, in terms of the competition between the elastic instability caused by nonaffine deformation of the glassy cage and the stress buildup due to viscous dissipation.
We numerically study the evolution of the vibrational density of states $D(omega)$ of zero-temperature glasses when their kinetic stability is varied over an extremely broad range, ranging from poorly annealed glasses obtained by instantaneous quenches from above the onset temperature, to ultrastable glasses obtained by quenching systems thermalised below the experimental glass temperature. The low-frequency part of the density of states splits between extended and quasi-localized modes. Extended modes exhibit a boson peak crossing over to Debye behaviour ($D(omega) sim omega^2$) at low-frequency, with a strong correlation between the two regimes. Quasi-localized modes instead obey $D(omega) sim omega^4$, irrespective of the glass stability. However, the prefactor of this quartic law becomes smaller in more stable glasses, and the corresponding modes become more localized and sparser. Our work is the first numerical observation of quasi-localized modes in a regime relevant to experiments, and it establishes a direct connection between glass stability and soft vibrational motion in amorphous solids.
We image local structural rearrangements in soft colloidal glasses under small periodic perturbations induced by thermal cycling. Local structural entropy $S_{2}$ positively correlates with observed rearrangements in colloidal glasses. The high $S_{2}$ values of the rearranging clusters in glasses indicate that fragile regions in glasses are structurally less correlated, similar to structural defects in crystalline solids. Slow-evolving high $S_{2}$ spots are capable of predicting local rearrangements long before the relaxations occur, while fluctuation-created high $S_{2}$ spots best correlate with local deformations right before the rearrangement events. Local free volumes are also found to correlate with particle rearrangements at extreme values, although the ability to identify relaxation sites is substantially lower than $S_{2}$. Our experiments provide an efficient structural identifier for the fragile regions in glasses, and highlight the important role of structural correlations in the physics of glasses.
Dynamics of protein self-assembly on the inorganic surface and the resultant geometric patterns are visualized using high-speed atomic force microscopy. The time dynamics of the classical macroscopic descriptors such as 2D Fast Fourier Transforms (FFT), correlation and pair distribution function are explored using the unsupervised linear unmixing, demonstrating the presence of static ordered and dynamic disordered phases and establishing their time dynamics. The deep learning (DL)-based workflow is developed to analyze detailed particle dynamics on the particle-by-particle level. Beyond the macroscopic descriptors, we utilize the knowledge of local particle geometries and configurations to explore the evolution of local geometries and reconstruct the interaction potential between the particles. Finally, we use the machine learning-based feature extraction to define particle neighborhood free of physics constraints. This approach allowed separating the possible classes of particle behavior, identify the associated transition probabilities, and further extend this analysis to identify slow modes and associated configurations, allowing for systematic exploration and predictive modeling of the time dynamics of the system. Overall, this work establishes the DL based workflow for the analysis of the self-organization processes in complex systems from observational data and provides insight into the fundamental mechanisms.
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