2019
DOI: 10.3389/fmats.2019.00141
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Machine Learning-Based Classification of Dislocation Microstructures

Abstract: Dislocations-the carrier of plastic deformation-are responsible for a wide range of mechanical properties of metals or semiconductors. Those line-like objects tend to form complex networks that are very difficult to characterize or to link to macroscopic properties on the specimen scale. In this work a machine learning based approach for classification of coarse-grained dislocation microstructures in terms of different dislocation density field variables is used. The performance of the model combined with doma… Show more

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Cited by 38 publications
(28 citation statements)
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“…3a shows the distribution of J in the simulated systems. The second used descriptor was GND density (Arsenlis and Parks 1999;Steinberger et al 2019). We computed the local GND density (the total GND density is constant throughout the simulation (Bulatov et al 2000)) by first evaluating the Nye tensor α in voxels by…”
Section: Characterizing Dislocation Structuresmentioning
confidence: 99%
See 1 more Smart Citation
“…3a shows the distribution of J in the simulated systems. The second used descriptor was GND density (Arsenlis and Parks 1999;Steinberger et al 2019). We computed the local GND density (the total GND density is constant throughout the simulation (Bulatov et al 2000)) by first evaluating the Nye tensor α in voxels by…”
Section: Characterizing Dislocation Structuresmentioning
confidence: 99%
“…To address this problem, we use machine learning (ML). ML is proving to be a flexible and useful tool for physics and materials science (Mehta et al 2019;Papanikolaou 2018;Papanikolaou et al 2019;Steinberger et al 2019;Yang et al 2020;Zhang and Ngan 2019;Zdeborová 2017). Using ML for the detection of phase transitions in statistical physics has given fruitful results (Carrasquilla and Melko 2017;Hu et al 2017;Shirinyan et al 2019) and here we applied the unsupervised 'confusion' scheme introduced in Van Nieuwenburg et al (2017).…”
Section: Introductionmentioning
confidence: 99%
“…In that work, dislocation classification was performed using Principal Component Analysis (PCA) 18 and continuous k-nearest neighbors clustering algorithms 19 . Analogous classifications of dislocation structures have since been extended in disordered dislocation environments 20 and three dimensional DDD samples 21 , as well as continuous plasticity models 22 using a variety of data science approaches. However, the problems in existing works are either in simpler models of dislocation dynamics 20 or with less challenging limits of the model (i.e.…”
mentioning
confidence: 99%
“…to characteristics of the underlying dislocation structure. Steinberger and co-authors [10] already demonstrated with machine learning techniques that the second order alignment tensor is of significance in distinguishing dislocation distributions. As for the curvature tensors we note for instance, that from the continuum theory we expect the vector q * (1) to be intimately related to dislocation multiplication and thus hardening.…”
Section: Discussionmentioning
confidence: 99%
“…These so-called dislocation alignment tensors [8] allow for a seamless characterisation of orientation distributions of dislocation lines. Some of these CDD variables have been obtained from DD simulations in [9] and [10], where in the latter the second order alignment tensor has been found to be of significance in a specific machine learning task. However, these two studies employed a special DD code based on parametrised dislocations and a method termed discreteto-continuum framework (D2C).…”
Section: Introductionmentioning
confidence: 99%