دورية أكاديمية
Computational Materials Design of High-Entropy Alloys Based on Full Potential Korringa-Kohn-Rostoker Coherent Potential Approximation and Machine Learning Techniques.
| العنوان: | Computational Materials Design of High-Entropy Alloys Based on Full Potential Korringa-Kohn-Rostoker Coherent Potential Approximation and Machine Learning Techniques. |
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| المؤلفون: | Kazunori Sato, Genta Hayashi, Kazuma Ogushi, Shuichi Okabe, Katsuhiro Suzuki, Tomoyuki Terai, Tetsuya Fukushima |
| المصدر: | Materials Transactions; 2023, Vol. 64 Issue 9, p2174-2178, 5p |
| مصطلحات موضوعية: | MACHINE learning, MONTE Carlo method, YOUNG'S modulus, DATABASES, ELECTRONIC structure, DESCRIPTOR systems |
| مستخلص: | Computational materials design (CMD) based on the first-principles electronic structure calculations is demonstrated for two topics related to the design of high-entropy alloys (HEAs). The first one is a construction of prediction model of elastic constants. By applying machine learning technique with the use of the linearly independent descriptor generation method to the database of elastic constants of 2555 BCC HEAs generated by the full potential Korringa-Kohn-Rostoker coherent potential approximation (FPKKR-CPA) method. The obtained model is used to predict new HEAs with high Young’s modulus. The second topic is a simulation of atomic arrangement in HEAs at finite temperature. In this simulation, HEAs are described by using the Potts-like model and the interaction parameters are determined based on the generalized perturbation method combined with the KKR-CPA method. Monte Carlo simulations for the models of CrMnFeCoNi and CrMnFeCoCu predict atomic arrangements which are consistent to the experimental observations. [ABSTRACT FROM AUTHOR] |
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| قاعدة البيانات: | Complementary Index |
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