دانلود مقاله انگلیسی رایگان:شناخت مرز دانه - نقش کریستالوگرافی ، توصیف کننده های ساختاری و یادگیری ماشین - 2019
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  • Understanding grain boundaries – The role of crystallography, structural descriptors and machine learning Understanding grain boundaries – The role of crystallography, structural descriptors and machine learning
    Understanding grain boundaries – The role of crystallography, structural descriptors and machine learning

    سال انتشار:

    2019


    عنوان انگلیسی مقاله:

    Understanding grain boundaries – The role of crystallography, structural descriptors and machine learning


    ترجمه فارسی عنوان مقاله:

    شناخت مرز دانه - نقش کریستالوگرافی ، توصیف کننده های ساختاری و یادگیری ماشین


    منبع:

    Sciencedirect - Elsevier - Computational Materials Science, 162 (2019) 281-294: doi:10:1016/j:commatsci:2019:02:047


    نویسنده:

    Srikanth Patala


    چکیده انگلیسی:

    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 highthroughput 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 crystallographystructure- property relationships for grain boundaries.
    Keywords: Grain boundaries | Crystallography | Machine learning | Atomic structure | Disconnections


    سطح: متوسط
    تعداد صفحات فایل pdf انگلیسی: 14
    حجم فایل: 4417 کیلوبایت

    قیمت: رایگان


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