Zystizerkenarteriitis

Wolf, George D.; Allert, M L; Faulhauer, K.; Schaefer, Kristina; Wettmann, U.

doi:10.1007/bf00245782

Cited by 7 publications

(4 citation statements)

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“…This result challenges the class wisdom [14] by suggesting that Σ is not an important parameter to characterize the properties of general GBs; however, this maverick finding is supported by a series of recent studies by Rohrer, Randle, and their co-workers [38][39][40][41]. Second, the GA found the orientations of the terminal GB planes to be important in general, which is consistent with recent theoretical [14,42,43] and experimental [14,16,18,44] studies. Third, the GA found that denser (lower Miller index) terminating grain plane is more important to control adsorption and structures of asymmetric AT and MX GBs, as well as all-included GBs.…”

Section: Genetic Algorithm (Ga) To Select Significant Gb Descriptorssupporting

confidence: 79%

“…GBs can be characterized by five macroscopic DOFs based on coincident-site-lattice (CSL) misorientation scheme or interface-plane scheme [14,15,35]. We primarily adopt the latter, where GBs are classified into four groups: ST, TW, AT, and MX GBs ( Fig.…”

Section: Genetic Algorithm (Ga) To Select Significant Gb Descriptorsmentioning

confidence: 99%

“…Interestingly, the GA also reproduced some common knowledge and conventions. For ST GBs, the GA-identified significant descriptors are all in the CSL-misorientation notation tlt [utlt vtlt wtlt] (h k l) commonly-used to denote ST GBs, where tlt and [utlt vtlt wtlt] are the tilt angle and axis, and (h k l) is the boundary plane [14]. Twist GBs only have five GA-selected significant descriptors, which are all in their characteristic structural relation of Σ = (Φtwst) 2 , where Φtwst is the twist angle (noting that Φtwst = mis for TW GBs), is the inverse planar coincident-site density [14], and = 1 or 0.5 to ensure Σ to be odd.…”

Section: Genetic Algorithm (Ga) To Select Significant Gb Descriptorsmentioning

confidence: 99%

“…However, a GB has five macroscopic (crystallographic) degrees of freedom (DOFs) [14,15], in addition to several thermodynamic DOFs, including temperature and the chemical potential(s) set by the bulk composition. Thus, it becomes virtually a "mission impossible" to construct bulk composition-and temperature-dependent property diagrams for GBs as functions of five crystallographic DOFs by either experiments or modeling.…”

Section: Introductionmentioning

confidence: 99%

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Genetic algorithm-guided deep learning of grain boundary diagrams: Addressing the challenge of five degrees of freedom

Zuo

Chen

et al. 2020

Materials Today

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Grain boundaries (GBs) often control the processing and properties of polycrystalline materials.Here, a potentially transformative research is represented by constructing GB property diagrams as functions of temperature and bulk composition, also called "complexion diagrams," as a general materials science tool on par with phase diagrams. However, a GB has five macroscopic (crystallographic) degrees of freedom (DOFs). It is essentially a "mission impossible" to construct property diagrams for GBs as a function of five DOFs by either experiments or modeling. Herein, we combine isobaric semi-grand-canonical ensemble hybrid Monte Carlo and molecular dynamics (hybrid MC/MD) simulations with a genetic algorithm (GA) and deep neural network (DNN) models to tackle this grand challenge. The DNN prediction is ~10 8 faster than atomistic simulations, thereby enabling the construction of the property diagrams for millions of distinctly different GBs of five DOFs. Notably, excellent prediction accuracies have been achieved for not only symmetric-tilt and twist GBs, but also asymmetric-tilt and mixed tilt-twist GBs; the latter are more complex and much less understood, but they are ubiquitous and often limit the performance properties of real polycrystals as the weak links. The data-driven prediction of GB properties as function of temperature, bulk composition, and five crystallographic DOFs (i.e., in a 7D space) opens a new paradigm.

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