Efficient global crystal structure prediction using polynomial machine learning potential in the binary AlCu alloy system
JOURNAL OF THE CERAMIC SOCIETY OF JAPAN(2023)
摘要
Machine learning potentials (MLPs) are attracting much attention as powerful tools to accurately and efficiently perform atomistic simulations and crystal structure predictions. In this study, we develop a polynomial MLP for the Al-Cu system applicable to the robust global structure search and metastable structure enumeration. We then apply a combination of a global optimization method and the polynomial MLP to the Al-Cu alloy system. As a result of approximately 1010 times energy computations, the globally-stable and metastable structures are enumerated in the Al-Cu system.(c) 2023 The Ceramic Society of Japan. All rights reserved.
更多查看译文
关键词
Crystal structure prediction,Machine learning potentials,Density functional theory (DFT) calculation,Alloy system
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要