Grain segmentation in atomistic simulations using orientation-based iterative self-organizing data analysis

Vimal, Manorama; Sandfeld, Stefan; Prakash, Aruna

doi:10.1016/j.mtla.2022.101314

Cited by 10 publications

(6 citation statements)

References 39 publications

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“…Finally, based on the PTM results, the crystallized models are further analyzed by the grain segmentation (GS) method. 57 This method provides more intuitive results for grain distribution. The analysis using the GS method shows that large grains are found in the Sb 2 Te phase, while small grains are found in the Sb 2 Te 2 phase (Fig.…”

Section: Resultsmentioning

confidence: 99%

Revealing the crystallization dynamics of Sb–Te phase change materials by large-scale simulations

Li,

Liu,

Zhou

et al. 2024

J. Mater. Chem. C

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Section: Resultsmentioning

confidence: 99%

Revealing the crystallization dynamics of Sb–Te phase change materials by large-scale simulations

Li,

Liu,

Zhou

et al. 2024

J. Mater. Chem. C

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“…Lastly, a grain analysis for the last configuration (at 6 ns) of all MD simulation cells modeled with the MEAM potential (Fig. 18) was computed using the grain segmentation algorithm reported by Vimal et al 103 Grain segmentation is compared to the Polyhedral Template Matching (PTM) results (shaded section in Fig. 18).…”

Section: Resultsmentioning

confidence: 99%

On the transferability of classical pairwise additive atomistic force field to the description of unary and multi-component systems: applications to the solidification of Al-based alloys

Sánchez

Rincent

Gheribi

et al. 2022

Phys. Chem. Chem. Phys.

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“…Identifying grains in deformed configurations of atomistic simulations using orientations of atoms as a measure is a challenging task. [ 50 ] Once such a domain is identified, the methodology presented in this work can be applied directly to assess the partitioning of fields in individual grains.…”

Section: Discussionmentioning

confidence: 99%

“…[44][45][46][47][48] Such machine learning methods can also help overcome the problem of obtaining the aforementioned information in an automated manner and facilitate knowledge transfer between the atomistic and continuum scales. [47][48][49][50] Additionally, one of the challenges in machine learning of "never enough data" is easily overcome because every atom is essentially a data point-i.e., fields of interest, like total and elastic strain, are calculated as properties of individual atoms-and typical simulations involve millions to billions of atoms. Mining such data will help understand the complex relationships between the emerging local fields-such as strain, stress, and texture-with the macroscopic response, and support in the formulation of microstructure-property relationships.…”

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confidence: 99%

Automated Analysis of Continuum Fields from Atomistic Simulations Using Statistical Machine Learning

Prakash

Sandfeld

2022

Adv Eng Mater

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Atomistic simulations of the molecular dynamics/statics kind are regularly used to study small‐scale plasticity. Contemporary simulations are performed with tens to hundreds of millions of atoms, with snapshots of these configurations written out at regular intervals for further analysis. Continuum scale constitutive models for material behavior can benefit from information on the atomic scale, in particular in terms of the deformation mechanisms, the accommodation of the total strain, and partitioning of stress and strain fields in individual grains. Herein, a methodology is developed using statistical data mining and machine learning algorithms to automate the analysis of continuum field variables in atomistic simulations. Three important field variables are focused on: total strain, elastic strain, and microrotation. The results show that the elastic strain in individual grains exhibits a unimodal lognormal distribution, while the total strain and microrotation fields evidence a multimodal distribution. The peaks in the distribution of total strain are identified with a Gaussian mixture model and methods to circumvent overfitting problems are presented. Subsequently, the identified peaks are evaluated in terms of deformation mechanisms in a grain, which, e.g., helps to quantify the strain for which individual deformation mechanisms are responsible. The overall statistics of the distributions over all grains are an important input for higher scale models, which ultimately also helps to be able to quantitatively discuss the implications for information transfer to phenomenological models.

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Grain segmentation in atomistic simulations using orientation-based iterative self-organizing data analysis

Cited by 10 publications

References 39 publications

Revealing the crystallization dynamics of Sb–Te phase change materials by large-scale simulations

Revealing the crystallization dynamics of Sb–Te phase change materials by large-scale simulations

On the transferability of classical pairwise additive atomistic force field to the description of unary and multi-component systems: applications to the solidification of Al-based alloys

Automated Analysis of Continuum Fields from Atomistic Simulations Using Statistical Machine Learning

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