A primer on selecting grain boundary sets for comparison of interfacial fracture properties in molecular dynamics simulations

Dingreville, Remi Philippe Michel; Aksoy, Doruk; Spearot, Douglas E.

doi:10.1038/s41598-017-08637-z

Cited by 24 publications

(12 citation statements)

References 43 publications

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“…Because of the symmetry of the grain boundaries constructed, this value is identical in the two grains for all boundaries studied here; however, for more general boundaries, the value of each and/or the ratio between them could likely be used as additional descriptors. The next seven macroscopic descriptors are an extension of those proposed by Dingreville et al for the study of grain boundaries. Six of these relate to the Schmid factor, another important factor in the failure of grain boundaries, as it represents which slip system will have the largest resolved shear stress. where ϕ and λ are the angles between the direction of loading with the plane and vector, respectively, under consideration.…”

Section: Descriptor Setsmentioning

confidence: 99%

Microscopic and Macroscopic Characterization of Grain Boundary Energy and Strength in Silicon Carbide via Machine-Learning Techniques

Guziewski

Zapiain

Dingreville

et al. 2021

ACS Appl. Mater. Interfaces

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Predicting the properties of grain boundaries poses a challenge because of the complex relationships between structural and chemical attributes both at the atomic and continuum scales. Grain boundary systems are typically characterized by parameters used to classify local atomic arrangements in order to extract features such as grain boundary energy or grain boundary strength. The present work utilizes a combination of high-throughput atomistic simulations, macroscopic and microscopic descriptors, and machine-learning techniques to characterize the energy and strength of silicon carbide grain boundaries. A diverse data set of symmetric tilt and twist grain boundaries are described using macroscopic metrics such as misorientation, the alignment of critical low-index planes, and the Schmid factor, but also in terms of microscopic metrics, by quantifying the local atomic structure and chemistry at the interface. These descriptors are used to create random-forest regression models, allowing for their relative importance to the grain boundary energy and decohesion stress to be better understood. Results show that while the energetics of the grain boundary were best described using the microscopic descriptors, the ability of the macroscopic descriptors to reasonably predict grain boundaries with low energy suggests a link between the crystallographic orientation and the resultant atomic structure that forms at the grain boundary within this regime. For grain boundary strength, neither microscopic nor macroscopic descriptors were able to fully capture the response individually. However, when both descriptor sets were utilized, the decohesion stress of the grain boundary could be accurately predicted. These results highlight the importance of considering both macroscopic and microscopic factors when constructing constitutive models for grain boundary systems, which has significant implications for both understanding the fundamental mechanisms at work and the ability to bridge length scales.

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Section: Descriptor Setsmentioning

confidence: 99%

Microscopic and Macroscopic Characterization of Grain Boundary Energy and Strength in Silicon Carbide via Machine-Learning Techniques

Guziewski

Zapiain

Dingreville

et al. 2021

ACS Appl. Mater. Interfaces

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“…At the macroscopic level, these descriptors include degrees of freedom that characterize the crystallography and compatibility conditions of the adjoining grains. 24,25 At the microscopic level, these descriptors categorize the atomic configuration and local environment of the grain boundary region, including, for instance, commonneighbor analysis (CNA), 26 polyhedral template matching, 27 Voronoi-cell topology, 28 or the Smooth Overlap of Atomic Positions (SOAP) formalism. 29−31 The SOAP descriptor is particularly well-suited to describe and compare grain boundary structures because it is invariant with respect to structural symmetries, rotations, and permutations, and it smoothly varies while accommodating structural perturbations.…”

Section: ■ Introductionmentioning

confidence: 99%

“…Successful implementation of machine learning algorithms requires a set of macroscopic and microscopic descriptors to accurately predict grain boundary properties. At the macroscopic level, these descriptors include degrees of freedom that characterize the crystallography and compatibility conditions of the adjoining grains. , At the microscopic level, these descriptors categorize the atomic configuration and local environment of the grain boundary region, including, for instance, common-neighbor analysis (CNA), polyhedral template matching, Voronoi-cell topology, or the Smooth Overlap of Atomic Positions (SOAP) formalism. − The SOAP descriptor is particularly well-suited to describe and compare grain boundary structures because it is invariant with respect to structural symmetries, rotations, and permutations, and it smoothly varies while accommodating structural perturbations. Recent studies − ,, have used the SOAP formalism to quantify and connect the atomic and crystallographic structures of single-element grain boundaries in their minimum energy configurations to properties such as grain boundary energy.…”

Section: Introductionmentioning

confidence: 99%

Characterizing the Tensile Strength of Metastable Grain Boundaries in Silicon Carbide Using Machine Learning

et al. 2020

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The local atomic structure, local chemistry, and stoichiometry of grain boundaries control in part the strength and fracture toughness of silicon carbide components. The predictions of the structure and properties of these grain boundaries are generally limited to their ground-state configurations. We investigated the tensile strength behavior of metastable grain boundaries in silicon carbide using high-throughput atomistic simulations combined with machine learning techniques. We analyzed and compared the ∑5 ⟨100⟩{120} and ∑9 ⟨110⟩{122} tilt grain boundary metastable configurations to identify structural and chemical attributes that dominate their tensile strength. We characterized these metastable grain boundaries using a set of microscopic descriptors representing the local grain boundary atomic structure and the local grain boundary stoichiometry and chemical-bound types. We used a boosted regression tree surrogate model for the successful prediction of metastable grain boundary strength as a function of these descriptors. Our results show that the tensile strength of generic (i.e., any random grain boundary from the entire grain boundary population), metastable grain boundaries is primarily dominated by the grain boundary excess free volume, closely followed by the type of structure composing the boundary and the amount of C–C bonds. The 5% strongest metastable grain boundaries have particular characteristics with a low amount of free volume and the highest density of C–C bonds. Our results reveal that the 5% strongest and weakest metastable grain boundaries are most sensitive to the local stoichiometry, regardless of the local atomic structure composing the grain boundary as compared to any other generic metastable grain boundaries. We show that the strongest and weakest metastable grain boundary configurations can be identified as specific regions in a low-dimensional-representation space of their microscopic descriptors. Taken together, these findings showcase the effectiveness and validity of using a low-dimensional representation of the grain boundary structure and machine-learned surrogate models to rapidly assess metastable grain boundary strength without the need to perform actual tensile simulations.

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“…Based on atomistic simulations, several computational efforts have focused on the hydrogen segregation and diffusion properties and embrittlement consequences for some selected grain boundary in nickel 19 , 38 – 57 . Classically, the grain boundaries are characterized by their energy, excess volume and geometric parameters such as Coincidence Site Lattice; easily accessible data using density functional theory (DFT) or molecular dynamics (MD) simulations 58 , 59 .…”

Section: Introductionmentioning

confidence: 99%

Antagonist effects of grain boundaries between the trapping process and the fast diffusion path in nickel bicrystals

Hallil

Metsue

et al. 2021

Sci Rep

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Hydrogen-grain-boundaries interactions and their role in intergranular fracture are well accepted as one of the key features in understanding hydrogen embrittlement in a large variety of common engineer situations. These interactions implicate some fundamental processes classified as segregation, trapping and diffusion of the solute which can be studied as a function of grain boundary configuration. In the present study, we carried out an extensive analysis of four grain-boundaries based on the complementary of atomistic calculations and experimental data. We demonstrate that elastic deformation has an important contribution on the segregation energy which cannot be simply reduced to a volume change and need to consider the deviatoric part of strain. Additionally, some significant configurations of the segregation energy depend on the long-range elastic distortion and allows to rationalize the elastic contribution in three terms. By investigating the different energy barriers involved to reach all the segregation sites, the antagonist impact of grain boundaries on hydrogen diffusion and trapping process was elucidated. The segregation energy and migration energy are two fundamental parameters in order to classify the grain-boundaries as a trapping location or short circuit for diffusion.

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A primer on selecting grain boundary sets for comparison of interfacial fracture properties in molecular dynamics simulations

Cited by 24 publications

References 43 publications

Microscopic and Macroscopic Characterization of Grain Boundary Energy and Strength in Silicon Carbide via Machine-Learning Techniques

Microscopic and Macroscopic Characterization of Grain Boundary Energy and Strength in Silicon Carbide via Machine-Learning Techniques

Characterizing the Tensile Strength of Metastable Grain Boundaries in Silicon Carbide Using Machine Learning

Antagonist effects of grain boundaries between the trapping process and the fast diffusion path in nickel bicrystals

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