Atomistic-to-meso multi-scale data-driven graph surrogate modeling of dislocation glide

Moraes, Eduardo A. Barros de; Suzuki, Jorge L.; Zayernouri, Mohsen

doi:10.1016/j.commatsci.2021.110569

Cited by 10 publications

(5 citation statements)

References 41 publications

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“…Aging results in the oxidation or loss of elastin, which leads to the loss of tissue elasticity such as in the vocal fold tissues [4,10]. Furthermore, in terms of multi-scale modeling, the lumped plastic behavior introduced here could potentially be coupled with existing discrete dislocation dynamics (DDD) models [7,8]. The models developed in this work uniquely qualify for simulating such characteristics, which will be done in our future work.…”

Section: Cpu Time [S]mentioning

confidence: 99%

A General Return-Mapping Framework for Fractional Visco-Elasto-Plasticity

Suzuki¹,

Naghibolhosseini²,

Zayernouri³

2022

Preprint

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We develop a fractional return-mapping framework for power-law visco-elastoplasticity. In our approach, the fractional viscoelasticity is accounted through canonical combinations of Scott-Blair elements to construct a series of well-known fractional linear viscoelastic models, such as Kelvin-Voigt, Maxwell, Kelvin-Zener and Poynting-Thomson. We also consider a fractional quasilinear version of Fung's model to account for stress/strain nonlinearity. The fractional viscoelastic models are combined with a fractional visco-plastic device, coupled with fractional viscoelastic models involving serial combinations of Scott-Blair elements. We then develop a general return-mapping procedure, which is fully implicit for linear viscoelastic models, and semi-implicit for the quasi-linear case. We find that, in the correction phase, the discrete stress projection and plastic slip have the same form for all the considered models, although with different property and time-step dependent projection terms. A series of numerical experiments is carried out with analytical and reference solutions to demonstrate the convergence and computational cost of the proposed framework, which is shown to be at least first-order accurate for general loading conditions. Our numerical results demonstrate that the developed framework is more flexible, preserves the numerical accuracy of existing approaches while being more computationally tractable in the visco-plastic range due to a reduction of 50% in CPU time. Our formulation is especially suited for emerging applications of fractional calculus in bio-tissues that present the hallmark of multiple viscoelastic power-laws coupled with visco-plasticity.

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Section: Cpu Time [S]mentioning

confidence: 99%

A General Return-Mapping Framework for Fractional Visco-Elasto-Plasticity

Suzuki¹,

Naghibolhosseini²,

Zayernouri³

2022

Preprint

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“…From the aforementioned trial states, each discretized viscoelastic constitutive laws ( 28)- (36) and recalling (39), one can show the following stress correction onto the yield surface:…”

Section: Generalized Fractional Return-mapping Algorithm (Algorithm 1)mentioning

confidence: 99%

“…Aging results in the oxidation or loss of elastin, which leads to the loss of tissue elasticity such as in the vocal fold tissues [37,38]. Further-more, in terms of multi-scale modeling, the lumped plastic behavior introduced here could potentially be coupled with existing discrete dislocation dynamics (DDD) models [39,40]. The models developed in this work uniquely qualify for simulating such characteristics, which will be undertaken in our future work.…”

mentioning

confidence: 99%

A General Return-Mapping Framework for Fractional Visco-Elasto-Plasticity

2022

Self Cite

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We develop a fractional return-mapping framework for power-law visco-elasto-plasticity. In our approach, the fractional viscoelasticity is accounted for through canonical combinations of Scott-Blair elements to construct a series of well-known fractional linear viscoelastic models, such as Kelvin–Voigt, Maxwell, Kelvin–Zener, and Poynting–Thomson. We also consider a fractional quasi-linear version of Fung’s model to account for stress/strain nonlinearity. The fractional viscoelastic models are combined with a fractional visco-plastic device, coupled with fractional viscoelastic models involving serial combinations of Scott-Blair elements. We then develop a general return-mapping procedure, which is fully implicit for linear viscoelastic models, and semi-implicit for the quasi-linear case. We find that, in the correction phase, the discrete stress projection and plastic slip have the same form for all the considered models, although with different property and time-step-dependent projection terms. A series of numerical experiments is carried out with analytical and reference solutions to demonstrate the convergence and computational cost of the proposed framework, which is shown to be at least first-order accurate for general loading conditions. Our numerical results demonstrate that the developed framework is more flexible and preserves the numerical accuracy of existing approaches while being more computationally tractable in the visco-plastic range due to a reduction of 50% in CPU time. Our formulation is especially suited for emerging applications of fractional calculus in bio-tissues that present the hallmark of multiple viscoelastic power-laws coupled with visco-plasticity.

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“…Previous studies have explored the possibility of using graph-theoretical techniques for learning speciőc aspects of dislocation glide. Moraes et al [32] developed a graph-based surrogate model for glide of edge dislocations in BCC Fe, trained to molecular dynamics (MD) data. In this representation, sub-domains within the MD model are represented using nodes on a ring graph.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Modeling of Dislocation Mobility from Atomistics using Physics-Informed Machine Learning

Tian,

Bagchi,

Myhill

et al. 2024

Preprint

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Dislocation mobility, which dictates the response of dislocations to an applied stress, is a fundamental property of crystalline materials that governs the evolution of plastic deformation. Traditional approaches for deriving mobility laws rely on phenomenological models of the underlying physics, whose free parameters are in turn fitted to a small number of intuition-driven atomic scale simulations under varying conditions of temperature and stress. This tedious and time-consuming approach becomes particularly cumbersome for materials with complex dependencies on stress, temperature, and local environment, such as body-centered cubic crystals (BCC) metals and alloys. In this paper, we present a novel, uncertainty quantification-driven active learning paradigm for learning dislocation mobility laws from automated high-throughput large-scale molecular dynamics simulations, using Graph Neural Networks (GNN) with a physics-informed architecture. We demonstrate that this Physics-informed Graph Neural Network (PI-GNN) framework captures the underlying physics more accurately compared to existing phenomenological mobility laws in BCC metals.

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Atomistic-to-meso multi-scale data-driven graph surrogate modeling of dislocation glide

Cited by 10 publications

References 41 publications

A General Return-Mapping Framework for Fractional Visco-Elasto-Plasticity

A General Return-Mapping Framework for Fractional Visco-Elasto-Plasticity

A General Return-Mapping Framework for Fractional Visco-Elasto-Plasticity

Data-Driven Modeling of Dislocation Mobility from Atomistics using Physics-Informed Machine Learning

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