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Register a new userStructure and size of the plastic zone formed during nanoindentation of a metallic glass
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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.
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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.
In our previous publication (Ref. 1) we have shown that the data for the normalized diffusion coefficient of the polymers, $D_p/D_{p0}$, falls on a master curve when plotted as a function of $h/lambda_d$, where $h$ is the mean interparticle distance and $lambda_d$ is a dynamic length scale. In the present note we show that also the normalized diffusion coefficient of the nanoparticles, $D_N/D_{N0}$, collapses on a master curve when plotted as a function of $h/R_h$, where $R_h$ is the hydrodynamic radius of the nanoparticles.
The structural arrest of a polymeric suspension might be driven by an increase of the cross--linker concentration, that drives the gel transition, as well as by an increase of the polymer density, that induces a glass transition. These dynamical continuous (gel) and discontinuous (glass) transitions might interfere, since the glass transition might occur within the gel phase, and the gel transition might be induced in a polymer suspension with glassy features. Here we study the interplay of these transitions by investigating via event--driven molecular dynamics simulation the relaxation dynamics of a polymeric suspension as a function of the cross--linker concentration and the monomer volume fraction. We show that the slow dynamics within the gel phase is characterized by a long sub-diffusive regime, which is due both to the crowding as well as to the presence of a percolating cluster. In this regime, the transition of structural arrest is found to occur either along the gel or along the glass line, depending on the length scale at which the dynamics is probed. Where the two line meet there is no apparent sign of higher order dynamical singularity. Logarithmic behavior typical of $A_{3}$ singularity appear inside the gel phase along the glass transition line. These findings seem to be related to the results of the mode coupling theory for the $F_{13}$ schematic model.
In order to characterize the geometrical mesh size $xi$, we simulate a solution of coarse-grained polymers with densities ranging from the dilute to the concentrated regime and for different chain lengths. Conventional ways to estimate $xi$ rely either on scaling assumptions which give $xi$ only up to an unknown multiplicative factor, or on measurements of the monomer density fluctuation correlation length $xi_c$. We determine $xi_c$ from the monomer structure factor and from the radial distribution function, and find that the identification $xi=xi_c$ is not justified outside of the semidilute regime. In order to better characterize $xi$, we compute the pore size distribution (PSD) following two different definitions, one by Torquato et al. (Ref.1) and one by Gubbins et al. (Ref.2). We show that the mean values of the two distributions, $langle r rangle_T$ and $langle r rangle_G$, both display the behavior predicted for $xi$ by scaling theory, and argue that $xi$ can be identified with either one of these quantities. This identification allows to interpret the PSD as the distribution of mesh sizes, a quantity which conventional methods cannot access. Finally, we show that it is possible to map a polymer solution on a system of hard or overlapping spheres, for which Torquatos PSD can be computed analytically and reproduces accurately the PSD of the solution. We give an expression that allows $langle r rangle_T$ to be estimated with great accuracy in the semidilute regime by knowing only the radius of gyration and the density of the polymers.
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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