Poisson’s ratio of metallic glasses under pressure and low temperature

Liu, X.F.; Wang, R.J.; Wang, W.H.

doi:10.1016/j.scriptamat.2009.11.010

Cited by 13 publications

(8 citation statements)

References 34 publications

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“…This can be interpreted from kinetic energy, since it favors atomic mobility and nucleation of shear transformation zones (STZs). The positive correlation between T and has been previously reported in the literature for conventional alloys and CuZr MGs 60 , 61 and it can be understood from the enhanced lateral strain at higher temperatures. CN and W exhibit positive variations with elastic and plastic properties.…”

Section: Resultssupporting

confidence: 72%

Machine learning modeling for the prediction of plastic properties in metallic glasses

Amigó

Palominos

Valencia

2023

Sci Rep

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Metallic glasses are one of the most interesting mechanical materials studied in the last years, but as amorphous solids, they differ strongly from their crystalline counterparts. This matter can be addressed with the development and application of predictive techniques capable to describe the plastic regime. Here, machine learning models were employed for the prediction of plastic properties in CuZr metallic glasses. To this aim, 100 different samples were subjected to tensile tests by means of molecular dynamics simulations. A total of 17 materials properties were calculated and explored using statistical analysis. Strong correlations were found for stoichiometry, temperature, structural, and elastic properties with plastic properties. Three regression models were employed for the prediction of six plastic properties. Linear and Ridge regressions delivered the better prediction capability, with coefficients of determination above $$\sim$$ ∼ 80% for three plastic properties, whereas Lasso regression rendered lower performance, with coefficients of determination above $$\sim$$ ∼ 60% for two plastic properties. Overall, our work shows that molecular dynamics simulations together with machine learning models can provide a framework for the prediction of plastic behavior of complex materials.

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Section: Resultssupporting

confidence: 72%

Machine learning modeling for the prediction of plastic properties in metallic glasses

Amigó

Palominos

Valencia

2023

Sci Rep

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“…In other words, there exist close links between the microstructural change and properties/ features of the MGs through the medium of elastic constants. 16,94,[99][100][101][102][103][104] On the other hand, there exists clear correlations in empirical levels among the elastic moduli, the glass formation and transition, the properties and features, and the strong/fragile characteristics of MGs. 13,14,16,17,44,45,71,105,106 These correlations confirm that the elastic moduli are key parameters for controlling the features and properties of MGs.…”

Section: E Elastic Perspectives On Metallic Glasses and Mg-forming Lmentioning

confidence: 99%

Correlation between relaxations and plastic deformation, and elastic model of flow in metallic glasses and glass-forming liquids

Wang¹

2011

Journal of Applied Physics

152

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We study the similarity and correlations between relaxations and plastic deformation in metallic glasses (MGs) and MG-forming liquids. It is shown that the microscope plastic events, the initiation and formation of shear bands, and the mechanical yield in MGs where the atomic sites are topologically unstable induced by applied stress, can be treated as the glass to supercooled liquid state transition induced by external shear stress. On the other hand, the glass transition, the primary and secondary relaxations, plastic deformation and yield can be attributed to the free volume increase induced flow, and the flow can be modeled as the activated hopping between the inherent states in the potential energy landscape. We then propose an extended elastic model to describe the flow based on the energy landscape theory. That is, the flow activation energy density is linear proportional to the instantaneous elastic moduli, and the activation energy density q E is determined to be a simple expression of q E ¼ 10 11 G þ 1 11 K. The model indicates that both shear and bulk moduli are critical parameters accounting for both the homogeneous and inhomogeneous flows in MGs and MG-forming liquids. The elastic model is experimentally certified. We show that the elastic perspectives offers a simple scenario for the flow in MGs and MG-forming liquids and are suggestive for understanding the glass transition, plastic deformation, and nature and characteristics of MGs V C

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“…Thus, as a guide, we plot the Poisson ratio's of the various alloys under discussion. Our computed T = 0 K crystalline Poisson ratios are expected to be systematically low relative to the corresponding glasses, as G decreases more rapidly than K as temperature increases 43 , and amorphous G and K are lower relative to crystalline values by around 30% and 10%, respectively. Here we see no systematic trend in the choice of Ta versus W (empty versus filled plotting symbols) but Ni generally has higher Poisson's ratio than Co (red versus blue).…”

Section: Binary Elastic Modulimentioning

confidence: 82%

First-principles calculation of elastic moduli of early-late transition metal alloys

et al. 2014

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Motivated by interest in the elastic properties of high strength amorphous metals, we examine the elastic properties of select crystalline phases. Using first principles methods, we calculate elastic moduli in various chemical systems containing transition metals, specifically early (Ta,W) and late (Co,Ni). Theoretically predicted alloy elastic properties are verified for Ni-Ta by comparison with experimental measurements using resonant ultrasound spectroscopy. Comparison of our computed elastic moduli with effective medium theories shows that alloying leads to enhancement of bulk moduli relative to averages of the pure elements, and considerable deviation of predicted and computed shear moduli. Specifically, we find an enhancement of bulk modulus relative to effective medium theory and propose a candidate system for high strength, ductile amorphous alloys. Trends in the elastic properties of chemical systems are analyzed using force constants, electronic densities of state and Crystal Overlap Hamilton Populations. We interpret our findings in terms of the electronic structure of the alloys.

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Poisson’s ratio of metallic glasses under pressure and low temperature

Cited by 13 publications

References 34 publications

Machine learning modeling for the prediction of plastic properties in metallic glasses

Machine learning modeling for the prediction of plastic properties in metallic glasses

Correlation between relaxations and plastic deformation, and elastic model of flow in metallic glasses and glass-forming liquids

First-principles calculation of elastic moduli of early-late transition metal alloys

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