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دسته بندی:
یادگیری ماشین - machine learning
سال انتشار:
2019
عنوان انگلیسی مقاله:
Applying a machine learning interatomic potential to unravel the effects of local lattice distortion on the elastic properties of multi-principal element alloys
ترجمه فارسی عنوان مقاله:
استفاده از پتانسیل متقابل یادگیری ماشینی برای آشکار کردن اثرات اعوجاج شبکه محلی بر خصوصیات الاستیک آلیاژهای عنصر چند اصلی
منبع:
Sciencedirect - Elsevier - Journal of Alloys and Compounds, 803 (2019) 1054-1062: doi:10:1016/j:jallcom:2019:06:318
نویسنده:
Mehdi Jafary-Zadeh a, *, Khoong Hong Khoo a, Robert Laskowski a, Paulo S. Branicio b, Alexander V. Shapeev
چکیده انگلیسی:
The concept of local lattice distortion (LLD) is of fundamental importance in the understanding of
properties of high-entropy alloys and, more generally, of multi-principal element alloys (MPEAs). Despite
previous experimental and computational efforts, the unambiguous evaluation of the static (due to
atomic size difference) and dynamic (due to thermal fluctuation) LLD is still elusive. Here, as a first step,
we develop a machine learning interatomic potential based on an efficient “learning-on-the-fly” scheme
for CoFeNi, a prototypical ternary MPEA. Using this potential, we perform molecular dynamics simulations
to calculate the elastic moduli of single- and polycrystalline CoFeNi. The results are in excellent
agreement with theoretical and experimental data. As a second step, we design a simulation framework
allowing the determination of the effects of static and dynamic LLD, thermal expansion, and chemical
short-range order on the elastic properties of our prototypical MPEA. The results indicate that not only
the average value of LLD, but also its probability distribution affect the elastic properties of MPEAs. In
addition, we show that a variety of commonly used LLD indicators, e.g., atomic strain, pair distribution
function, and bond-length distribution, correlate with each other. Our results not only shed light on the
of LLD in MPEAs, but also demonstrate the capabilities of our machine learning potential as a powerful
tool for the development and characterization of novel alloys with designed properties.
Keywords: Multi-principal element alloys | High-entropy alloys | Elastic properties | Atomistic simulations | Machine learning
قیمت: رایگان
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