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