Machine learning aided high-throughput first-principles calculations to predict the formation enthalpy of σ phase
Author:
Publisher
Elsevier BV
Subject
Computer Science Applications,General Chemical Engineering,General Chemistry
Reference57 articles.
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3. Effects of rhenium and high-angle grain boundaries upon the elemental distribution and microstructure of Ni-based single-crystal superalloys;Liu;Mater. Char.,2023
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5. Characterization of the invariant reaction involving the L, η, γ and σ phases in the Ti-Al-Nb system;Rios;Acta Mater.,2009
Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. Applying enhanced active learning to predict formation energy;Computational Materials Science;2024-02
2. Machine Learning-Aided High-Throughput First-Principles Calculations to Predict the Formation Energy of μ Phase;ACS Omega;2023-09-27
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