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• W2920808840 endingPage "294" @default.
• W2920808840 startingPage "281" @default.
• W2920808840 abstract "Grain boundaries (GBs) influence a wide array of physical properties in polycrystalline materials and play an important role in governing microstructural evolution under extreme environments. While the importance of interfaces is well documented, their properties are among the least understood of all the defect types present in engineering material systems. This is due to the vast configurational space of interfaces, resulting in a diverse range of structures and properties. The complexity associated with GB structures is related to the different crystallographic degrees of freedom – the misorientation, the boundary-plane orientation and the relative translations between the adjoining crystals. These unique challenges can be addressed by leveraging high-throughput simulations of GB properties and developing machine learning algorithms grounded in the concepts of bicrystallography of interfaces. To demystify the relationships between crystallography and properties, the symmetry aspects of GBs are reviewed with an emphasis on boundary-plane orientations and disconnection line defects. To quantify structure-property relationships, recent advances in describing GB structures using the polyhedral unit model and the gaussian-based approximation of local atomic environments are discussed. Finally, examples of predicting GB structure-property relationships using machine learning techniques are summarized. As part of a special issue, the goal of this review article is to motivate machine learning strategies that are informed by bicrystallography and novel structural descriptors for developing reliable crystallography-structure-property relationships for grain boundaries." @default.
• W2920808840 created "2019-03-22" @default.
• W2920808840 creator A5063813038 @default.
• W2920808840 date "2019-05-01" @default.
• W2920808840 modified "2023-10-02" @default.
• W2920808840 title "Understanding grain boundaries – The role of crystallography, structural descriptors and machine learning" @default.
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