Nonequilibrium plastic roughening of metallic glasses yields self-affine topographies with strain-rate and temperature-dependent scaling exponents

We study nonequilibrium roughening during compressive plastic flow of initially flat Cu$_{50}$ Zr$_{50}$ metallic glass using large-scale molecular dynamics simulations. Roughness emerges at atomically flat interfaces beyond the yield point of the glass. A self-affine rough topography is imprinted at yield and is reinforced during subsequent deformation. The imprinted topographies have Hurst exponents that decrease with increasing strain rate and temperature. After yield, the root-mean-square roughness amplitude grows as the square root of the applied strain with a prefactor that also drops with increasing strain rate and temperature. Our calculations reveal the emergence of spatial power-law correlations from homogeneous samples during plastic flow with exponents that depend on the rate of deformation and the temperature. The results have implications for interpreting and engineering roughness profiles

Wedge-shaped twins and pseudoelasticity in fcc metallic nanowires under bending

AbstractMolecular dynamics simulations were performed to study the deformation mechanisms of 〈110〉-oriented, faceted Cu and Au nanowires under bending along three different crystallographic directions. Independent of the bending direction, the stress field is characterized by a highly nonlinear elastic response, leading to a shift of the neutral fiber away from the central wire axis. The nanowires show ultra-high yield strengths, and the achievable large elastic strains directly influence the dislocation nucleation through the change of the unstable stacking fault energy. In agreement with theory and experiments on face-centered cubic 〈110〉 nanowires under uniaxial load, the tensile part of the wires exhibit deformation twinning, while plastic deformation in the compressed part takes place by slip of perfect dislocations. Independent of the bending direction, wire size, temperature and bending rate, all wires showed the formation of wedge-shaped twins. Upon instantaneous load removal, wires bent in two of the three directions showed spontaneous, pseudoelastic unbending. The findings of this study could be relevant for the design of flexible electronics and mechanical energy storage applications at the nanoscale

The emergence of small-scale self-affine surface roughness from deformation

Most natural and man-made surfaces appear to be rough on many length scales. There is presently no unifying theory of the origin of roughness or the self-affine nature of surface topography. One likely contributor to the formation of roughness is deformation, which underlies many processes that shape surfaces such as machining, fracture, and wear. Using molecular dynamics, we simulate the biaxial compression of single-crystal Au, the high-entropy alloy Ni36.67Co30Fe16.67Ti16.67, and amorphous Cu50Zr50 and show that even surfaces of homogeneous materials develop a self-affine structure. By characterizing subsurface deformation, we connect the self-affinity of the surface to the spatial correlation of deformation events occurring within the bulk and present scaling relations for the evolution of roughness with strain. These results open routes toward interpreting and engineering roughness profiles

contact.engineering -- Create, analyze and publish digital surface twins from topography measurements across many scales

The optimization of surface finish to improve performance occurs largely through trial and error, despite significant advancements in the relevant science. There are three central challenges that account for this disconnect: (1) the challenge of integration of many different types of measurement for the same surface to capture the multi-scale nature of roughness; (2) the technical complexity of implementing spectral analysis methods, and of applying mechanical or numerical models to describe surface performance; (3) a lack of consistency between researchers and industries in how surfaces are measured, quantified, and communicated. Here we present a freely-available internet-based application which attempts to overcome all three challenges. First, the application enables the user to upload many different topography measurements taken from a single surface, including using different techniques, and then integrates all of them together to create a digital surface twin. Second, the application calculates many of the commonly used topography metrics, such as root-mean-square parameters, power spectral density (PSD), and autocorrelation function (ACF), as well as implementing analytical and numerical calculations, such as boundary element modeling (BEM) for elastic and plastic deformation. Third, the application serves as a repository for users to securely store surfaces, and if they choose, to share these with collaborators or even publish them (with a digital object identifier) for all to access. The primary goal of this application is to enable researchers and manufacturers to quickly and easily apply cutting-edge tools for the characterization and properties-modeling of real-world surfaces. An additional goal is to advance the use of open-science principles in surface engineering by providing a FAIR database where researchers can choose to publish surface measurements for all to use.Comment: 19 pages, 6 figure

matscipy : materials science at the atomic scale with Python

Behaviour of materials is governed by physical phenomena that occur at an extreme range of length and time scales. Computational modelling requires multiscale approaches. Simulation techniques operating on the atomic scale serve as a foundation for such approaches, providing necessary parameters for upper-scale models. The physical models employed for atomic simulations can vary from electronic structure calculations to empirical force fields. However, construction, manipulation and analysis of atomic systems are independent of the given physical model but dependent on the specific application. matscipy implements such tools for applications in materials science, including fracture, plasticity, tribology and electrochemistry

Nonequilibrium plastic roughening of metallic glasses yields self-affine topographies with strain-rate and temperature-dependent scaling exponents

We study nonequilibrium roughening during compressive plastic flow of initially flat Cu$_{50}$Zr$_{50}$ metallic glass using large-scale molecular dynamics simulations. Roughness emerges at atomically flat interfaces beyond the yield point of the glass. A self-affine rough topography is imprinted at yield and is reinforced during subsequent deformation. The imprinted topographies have Hurst exponents that decrease with increasing strain-rate and temperature. After yield, the root-mean-square roughness amplitude grows as the square-root of the applied strain with a prefactor that also drops with increasing strain-rate and temperature. Our calculations reveal the emergence of spatial power-law correlations from homogeneous samples during plastic flow with exponents that depend on the rate of deformation and the temperature. The results have implications for interpreting and engineering roughness profiles.Comment: 11 pages, 4 figure

Scale-dependent roughness parameters for topography analysis

The failure of roughness parameters to predict surface properties stems from their inherent scale-dependence; in other words, the measured value depends on the way it was measured. Here we take advantage of this scale-dependence to develop a new framework for characterizing rough surfaces: the Scale-Dependent Roughness Parameters (SDRP) analysis that yields slope, curvature and higher-order derivatives of surface topography at many scales, even on a single topography measurement. We demonstrate the relationship between SDRP and other common statistical methods for analyzing surfaces: the height-difference autocorrelation function (ACF), variable bandwidth methods (VBMs) and the power spectral density (PSD). We use computer-generated and measured topographies to demonstrate the benefits of SDRP analysis, including: novel metrics for characterizing surfaces across scales, and the detection of measurement artifacts. The SDRP is a generalized framework for scale-dependent analysis of surface topography that yields metrics that are intuitively understandable.Comment: 12 pages, 6 figure