دانلود مقاله انگلیسی رایگان:اصول اول و پیش بینی یادگیری ماشین از الاستیسیته در آلیاژهای آنتروپی با تحریف شدید شبکه با استفاده از اعتبار سنجی تجربی - 2019
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  • First-principles and Machine Learning Predictions of Elasticity in Severely Lattice-distorted High-Entropy Alloys with Experimental Validation First-principles and Machine Learning Predictions of Elasticity in Severely Lattice-distorted High-Entropy Alloys with Experimental Validation
    First-principles and Machine Learning Predictions of Elasticity in Severely Lattice-distorted High-Entropy Alloys with Experimental Validation

    سال انتشار:

    2019


    عنوان انگلیسی مقاله:

    First-principles and Machine Learning Predictions of Elasticity in Severely Lattice-distorted High-Entropy Alloys with Experimental Validation


    ترجمه فارسی عنوان مقاله:

    اصول اول و پیش بینی یادگیری ماشین از الاستیسیته در آلیاژهای آنتروپی با تحریف شدید شبکه با استفاده از اعتبار سنجی تجربی


    منبع:

    Sciencedirect - Elsevier - © 2019 Published by Elsevier Ltd on behalf of Acta Materialia Inc:


    نویسنده:

    George Kim , Haoyan Diao , Chanho Lee , A.T. Samaei , Tu Phan , Maarten de Jong , Ke An , Dong Ma , Peter K. Liaw , Wei Chen


    چکیده انگلیسی:

    Stiffness usually increases with the lattice-distortion-induced strain, as observed in many nanostructures. Partly due to the size differences in the component elements, severe lattice distortion naturally exists in high entropy alloys (HEAs). The single-phase face-centered-cubic (FCC) Al0.3CoCrFeNi HEA, which has large size differences among its constituent elements, is an ideal system to study the relationship between the elastic properties and lattice distortion using a combined experimental and computational approach based on in-situ neutron-diffraction (ND) characterizations, and first-principles calculations. Analysis of the interatomic distance distributions from calculations of optimized special quasi random structure (SQS) found that the HEA has a high degree of lattice distortion. When the lattice distortion is explicitly considered, elastic properties calculated using SQS are in excellent agreement with experimental measurements for the HEA. The calculated elastic constant values are within 5% of the ND measurements. A comparison of calculations from the optimized SQS and the SQS with ideal lattice sites indicate that the lattice distortion results in the reduced stiffness. The optimized SQS has a bulk modulus of 177 GPa compared to the ideal lattice SQS with a bulk modulus of 194 GPa. Machine learning (ML) modeling is also implemented to explore the use of fast, and computationally efficient models for predicting the elastic moduli of HEAs. ML models trained on a large dataset of inorganic structures are shown to make accurate predictions of elastic properties for the HEA. The ML models also demonstrate the dependence of bulk and shear moduli on several material features which can act as guides for tuning elastic properties in HEAs.
    Keywords: First-principles calculation | Elastic constants | in situ tension test | Neutron diffraction | Machine learning


    سطح: متوسط
    تعداد صفحات فایل pdf انگلیسی: 41
    حجم فایل: 1900 کیلوبایت

    قیمت: رایگان


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