Quantifying and connecting atomic and crystallographic grain boundary structure using local environment representation and dimensionality reduction techniques

Priedeman, Jonathan L.; Rosenbrock, Conrad W.; Johnson, Oliver K.; Homer, Eric R.

doi:10.1016/j.actamat.2018.09.011

Cited by 35 publications

(38 citation statements)

References 42 publications

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“…In other words, the range of LAEs provided by a sufficiently large and diverse sample of GB structures (92 in our case) is expected to encompass those encountered in GBs with other misorientations, higher complexity or lower symmetry. This result is consistent with Priedman et al's observation that different GBs consist of similar structural building blocks or motifs 41 . Consequently, similar to Rosenbrock et al's 38 findings for GB energies and mobilities, the properties and behaviour of individual GBs of MgO can be expected to depend on the relative numbers of each type of LAE of which they are composed.…”

Section: Resultssupporting

confidence: 92%

“…Compiling SOAP vectors of atoms in the GB model into a matrix known as the local environment representation allows each particular GB structure to be described quantitatively and uniquely 38,41 . One of the advantages of the SOAP descriptor is that it also makes it possible to compare LAEs quantitatively, so that a dissimilarity (or, conversely, similarity) metric can be defined between two atoms 33 which varies smoothly with a change in neighbouring atom positions 38 .…”

Section: Discussionmentioning

confidence: 99%

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Quantitative prediction of grain boundary thermal conductivities from local atomic environments

et al. 2020

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Quantifying the dependence of thermal conductivity on grain boundary (GB) structure is critical for controlling nanoscale thermal transport in many technologically important materials. A major obstacle to determining such a relationship is the lack of a robust and physically intuitive structure descriptor capable of distinguishing between disparate GB structures. We demonstrate that a microscopic structure metric, the local distortion factor, correlates well with atomically decomposed thermal conductivities obtained from perturbed molecular dynamics for a wide variety of MgO GBs. Based on this correlation, a model for accurately predicting thermal conductivity of GBs is constructed using machine learning techniques. The model reveals that small distortions to local atomic environments are sufficient to reduce overall thermal conductivity dramatically. The method developed should enable more precise design of next-generation thermal materials as it allows GB structures exhibiting the desired thermal transport behaviour to be identified with small computational overhead.

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Section: Resultssupporting

confidence: 92%

Section: Discussionmentioning

confidence: 99%

Quantitative prediction of grain boundary thermal conductivities from local atomic environments

et al. 2020

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“…To adequately analyze the local atomic structure of defects, such as GBs, a method is needed that can classify atoms without a priori knowledge of the structures present (i.e., without reliance on a small precomputed list of known structures). Several recent publications have presented methods to identify arbitrary local environments (Bartók et al, 2013;Banadaki and Patala, 2017;Reinhart et al, 2017;Rosenbrock et al, 2017;Priedeman et al, 2018), and a brief description of each is given here. Bartók et al (2013) developed an atomic structure descriptor based on the superposition of Gaussian kernels centered at atomic positions, referred to as the SOAP kernel/descriptor.…”

Section: Identification Of Non-crystalline Atomic Environmentsmentioning

confidence: 99%

“…SOAP is unique in that it is a continuous descriptor (making it robust against small changes in atomic positions) unlike most other descriptors that are discrete in nature. SOAP has recently been applied to characterize GBs by Rosenbrock et al (2017) and Priedeman et al (2018). Banadaki and Patala (2017) presented the polyhedral unit model, which compares the neighborhood around voids in atomic structures (at which vertices in the Voronoi tessellation are centered) against an exhaustive library of configurations of close-packed spheres for up to 12 spheres.…”

Section: Identification Of Non-crystalline Atomic Environmentsmentioning

confidence: 99%

“…As demonstrated above, common crystal structure identification techniques are insufficient for this task. Consequently, several authors have developed methods to identify arbitrary non-crystalline atomic structures for applications such as developing interatomic potentials (Bartók et al, 2013), analyzing colloidal crystallization (Reinhart et al, 2017), and characterizing grain boundaries (Banadaki and Patala, 2017;Rosenbrock et al, 2017;Priedeman et al, 2018). A brief summary of their work is given in section 2.…”

Section: Introductionmentioning

confidence: 99%

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A Simple Approach to Atomic Structure Characterization for Machine Learning of Grain Boundary Structure-Property Models

2019

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Grain boundaries (GBs) have a significant influence on the properties of crystalline materials. Machine learning approaches present an attractive route to develop atomic structure-property models for GBs because of the complexity of their structure. However, the application of such techniques requires an appropriate descriptor of the atomic structure. Unfortunately, common crystal structure identification techniques cannot be applied to characterize the structure of the vast majority of GB atoms (50-98% are classified as "other"). This suggests a critical need for atomic structure descriptors capable of identifying arbitrary atomic environments. In this work we present a simple procedure that facilitates the identification of arbitrary atomic structures present in GBs. We apply this approach to characterize the atomic structure of the 388 GBs from the Olmsted data set (Olmsted et al., 2009). We show how this approach facilitates visualization of GB atomic structures in a way that reveals important structural information. We test the recently proposed hypothesis that 3 GBs contain facets of the GBs that form the corners of the corresponding GB plane fundamental zone. Finally, we briefly demonstrate how the structure descriptors resulting from our approach can be used as inputs to machine learning approaches for the development of atomic structure-property models for GBs.

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Interplay Between Grain Boundaries and Radiation Damage

Barr

El-Atwani

Kaoumi

et al. 2019

JOM

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Quantifying and connecting atomic and crystallographic grain boundary structure using local environment representation and dimensionality reduction techniques

Cited by 35 publications

References 42 publications

Quantitative prediction of grain boundary thermal conductivities from local atomic environments

Quantitative prediction of grain boundary thermal conductivities from local atomic environments

A Simple Approach to Atomic Structure Characterization for Machine Learning of Grain Boundary Structure-Property Models

Interplay Between Grain Boundaries and Radiation Damage

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