Disconnection-mediated twin embryo growth in Mg

Hu, Yang; Turlo, Vladyslav; Beyerlein, Irene J.; Mahajan, S.; Lavernia, Enrique J.; Schoenung, Julie M.; Rupert, Timothy J.

doi:10.1016/j.actamat.2020.04.010

Cited by 30 publications

(34 citation statements)

References 53 publications

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“…1(b) shows the morphology of the twin embryo, which is bound by twin boundaries (TBs), conjugate TBs (c-TBs, almost 90° to the TBs), basal-prismatic (BP), and prismatic-basal (PB) interfaces. Our previous findings on twin embryo growth in pure Mg indicate that the initial twin embryo shape and size does not alter its growth mechanism [18].…”

mentioning

confidence: 84%

“…In our previous work on twin embryo growth in pure Mg [18], the lateral propagation of c-TBs was observed to be faster than the migration of TBs, which leads to TBs becoming the primary boundaries as the twin grows and matures. TB motion occurred by disconnection propagation, and a logarithmic relation between twin length and thickness was obtained.…”

mentioning

confidence: 91%

“…To reveal the atomistic mechanisms behind random twin variant selection in the highconcentration alloy samples, we focus on the effect of solute atoms on the nucleation and propagation of twinning disconnections, since these two processes drive the twin embryo growth [18]. Twinning disconnections are defined as interfacial dislocations with step character and account for the formation and migration of TBs [23][24][25].…”

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confidence: 99%

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Embracing the Chaos: Alloying Adds Stochasticity to Twin Embryo Growth

Turlo

Beyerlein

et al. 2020

Phys. Rev. Lett.

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High-throughput atomistic simulations reveal the unique effect of solute atoms on twin variant selection in Mg-Al alloys. Twin embryos first undergo a stochastic incubation stage when embryos choose which twin variant to adopt, and then a deterministic growth stage when embryos expand without changing the selected twin variant. An increase in Al composition promotes the stochastic incubation behavior on the atomic level due to nucleation and pinning of interfacial disconnections. At compositions above a critical value, disconnection pinning results in multiple twin variant selection.

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confidence: 84%

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confidence: 91%

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confidence: 99%

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Embracing the Chaos: Alloying Adds Stochasticity to Twin Embryo Growth

Turlo

Beyerlein

et al. 2020

Phys. Rev. Lett.

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“…As with plasticity by dislocation motion, boundary motion relies on disconnection nucleation in addition to propagation. Disconnections can nucleate in many ways; grain boundary dislocations [64,65], triple junctions [66], or other interfaces [67] can act as disconnection sources. However for large, pristine boundaries, disconnection nucleation becomes primarily a thermally activated process.…”

Section: B Nucleation Modelmentioning

confidence: 99%

Multiphase field modeling of grain boundary migration mediated by emergent disconnections

Gokuli

Runnels

2021

Acta Materialia

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Knowledge about grain boundary migration is a prerequisite for understanding and ultimately modulating the properties of polycrystalline materials. Evidence from experiments and molecular dynamics (MD) simulations suggests that the formation and motion of disconnections is a mechanism for grain boundary migration. Here, grain boundary migration is modeled using a multiphase field model based on the principle of minimum dissipation potential with nonconvex boundary energy, along with a stochastic model for thermal nucleation of disconnection pairs. In this model, disconnections arise spontaneously in the presence of an elastic driving force, and that their motion mediates boundary migration. The effect is due to the fact that the formation of the disconnections pairs results in a stress concentration, causing the elastic driving force to exceed the threshold value and driving the propagation of the disconnection along the interface. The model is applied to study the propagation/annihilation of single disconnection pairs, the relaxation of a perturbed interface, and shear coupling at various temperatures. The results are consistent with the current understanding of disconnections, and capture the effect of thermal softening.

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“…A twin embryo is created in Mg, and this software package is used to detect and characterize the grain boundary. The structure was generated using the Eshelby method [32,33] and relaxed using the potential described in Ref. [28].…”

Section: Study Of Volume Defectsmentioning

confidence: 99%

ClasSOMfier: A neural network for cluster analysis and detection of lattice defects

Troncoso

2020

Preprint

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ClasSOMfier is a software package to classify atoms into a given number of disconnected groups (or clusters) and detect lattice defects, such as vacancies, interstitials, dislocations, voids and grain boundaries. Each cluster is formed by atoms whose atomic environment can be described by a common pattern. Unlike many methods available in the literature, where these patterns are given in advance and are associated with known lattice structures (i.e. fcc, bcc or hcp), this code implements a Kohonen network, which is based on unsupervised learning and where no information about the atomic environment has to be given in advance. ClasSOMfier accelerates the application of machine learning for cluster analysis by providing an efficient and fast code in Fortran with a user-friendly interface in Python.

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Disconnection-mediated twin embryo growth in Mg

Cited by 30 publications

References 53 publications

Embracing the Chaos: Alloying Adds Stochasticity to Twin Embryo Growth

Embracing the Chaos: Alloying Adds Stochasticity to Twin Embryo Growth

Multiphase field modeling of grain boundary migration mediated by emergent disconnections

ClasSOMfier: A neural network for cluster analysis and detection of lattice defects

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