Constitutive relations for plasticity of amorphous carbon

Jana, Richard; Lautz, Julian von; Khosrownejad, S. Mostafa; Andrews, W. Beck; Moseler, Michael; Pastewka, Lars

doi:10.1088/2515-7639/ab953c

Cited by 5 publications

(7 citation statements)

References 47 publications

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“… 268 Another study included the generation of many individual a-C model structures by GAP-driven quenching and a subsequent link to experimental properties. 269 A computational study of plasticity in large structural models of a-C, again using a combination of GAP and a faster empirical potential, was reported in ref ( 270 ).…”

Section: Applications (I): Force Fieldsmentioning

confidence: 99%

Gaussian Process Regression for Materials and Molecules

et al. 2021

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We provide an introduction to Gaussian process regression (GPR) machine-learning methods in computational materials science and chemistry. The focus of the present review is on the regression of atomistic properties: in particular, on the construction of interatomic potentials, or force fields, in the Gaussian Approximation Potential (GAP) framework; beyond this, we also discuss the fitting of arbitrary scalar, vectorial, and tensorial quantities. Methodological aspects of reference data generation, representation, and regression, as well as the question of how a data-driven model may be validated, are reviewed and critically discussed. A survey of applications to a variety of research questions in chemistry and materials science illustrates the rapid growth in the field. A vision is outlined for the development of the methodology in the years to come.

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Section: Applications (I): Force Fieldsmentioning

confidence: 99%

Gaussian Process Regression for Materials and Molecules

et al. 2021

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“…Mechanical properties of a-C have been reported in previous works 34,35 at room temperature. However, all of our fracture simulations are performed at zero temperature.…”

Section: B Calculation Of Mechanical Propertiesmentioning

confidence: 82%

“…Furthermore, it has been shown that the flow stress of a-C de-pends on the internal pressure of the cell which can be built up during quenching and mechanical loading. 35 Specifically Jana et al 35 observed a Drucker-Prager law with a zero-pressure shear flow stress of 41.2 GPa and an internal friction coefficient of 0.39. Thus the stress is a function of internal pressure and relaxing the internal pressure during the mechanical loading would decrease the measured stresses.…”

Section: B Calculation Of Mechanical Propertiesmentioning

confidence: 99%

“…Both techniques yield K c I , but values converge faster with respect to system size for flexible than for rigid boundaries. 33 A systematic study of elasto-plastic behaviour of a-C with different densities was recently performed by Jana et al, 34,35 showing that the stress sustained during steady-state flow of a-C follows a Drucker-Prager law at room temperature. 36 Furthermore, we have also performed uniaxial tests at 0 K to measure the yield stress of a-C. From these results, we estimate that the process zone radius in mode I fracture for a stress intensity factor K I = 10 MPa √ m, which is in the range of experimental results, is less than 22 nm.…”

Section: Introductionmentioning

confidence: 99%

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Quantitative prediction of the fracture toughness of amorphous carbon from atomic-scale simulations

Khosrownejad,

Kermode,

Pastewka

2020

Preprint

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Fracture is the ultimate source of failure of amorphous carbon (a-C) films, however it is challenging to measure fracture properties of a-C from nano-indentation tests and results of reported experiments are not consistent. Here, we use atomic-scale simulations to make quantitative and mechanistic predictions on fracture of a-C. Systematic large-scale K-field controlled atomic-scale simulations of crack propagation are performed for a-C samples with densities of ρ = 2.5, 3.0 and 3.5 g/cm 3 created by liquid quenches for a range of quench rates Ṫq = 10 − 1000 K/ps. The simulations show that the crack propagates by nucleation, growth, and coalescence of voids. Distances of ≈ 1 nm between nucleated voids result in a brittle-like fracture toughness. We use a crack growth criterion proposed by Drugan, Rice & Sham to estimate steady-state fracture toughness based on our short crack-length fracture simulations. Fracture toughness values of 2.4 − 6.0 MPa √ m for initiation and 3 − 10 MPa √ m for the steady-state crack growth are within the experimentally reported range. These findings demonstrate that atomic-scale simulations can provide quantitatively predictive results even for fracture of materials with a ductile crack propagation mechanism.

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“…An ML model constructed using the GAP methodology is obtained for amorphous carbon 88 . Deformation of the representative volume element of amorphous carbon is achieved by melt quenching using bond order and ML of the interaction potential between atoms 89 . The structure and elastic properties of low‐speed quenched amorphous carbon are investigated 90 …”

Section: Applicationsmentioning

confidence: 99%

Machine‐learning‐based interatomic potentials for advanced manufacturing

Wan

et al. 2021

Int Journal of Mech Sys Dyn

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This paper summarizes the progress of machine-learning-based interatomic potentials and their applications in advanced manufacturing. Interatomic potential is essential for classical molecular dynamics. The advancements made in machine learning (ML) have enabled the development of fast interatomic potential with ab initio accuracy. The accelerated atomic simulation can greatly transform the design principle of manufacturing technology. The most widely used supervised and unsupervised ML methods are summarized and compared. Then, the emerging interatomic models based on ML are discussed: Gaussian approximation potential, spectral neighbor analysis potential, deep potential molecular dynamics, SCHNET, hierarchically interacting particle neural network, and fast learning of atomistic rare events.

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Constitutive relations for plasticity of amorphous carbon

Cited by 5 publications

References 47 publications

Gaussian Process Regression for Materials and Molecules

Gaussian Process Regression for Materials and Molecules

Quantitative prediction of the fracture toughness of amorphous carbon from atomic-scale simulations

Machine‐learning‐based interatomic potentials for advanced manufacturing

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