Molecular Dynamics Simulation of the Transformation of Fe-Co Alloy by Machine Learning Force Field Based on Atomic Cluster Expansion
Chemical physics letters(2023)
摘要
The force field describing the calculated interaction between atoms or molecules is the key to the accuracy of many molecular dynamics (MD) simulation results. Compared with traditional or semi-empirical force fields, machine learning force fields have the advantages of faster speed and higher precision. We have employed the method of atomic cluster expansion (ACE) combined with first-principles density functional theory (DFT) calculations for machine learning, and successfully obtained the force field of the binary Fe-Co alloy. Molecular dynamics simulations of Fe-Co alloy carried out using this ACE force field predicted the correct phase transition range of Fe-Co alloy.
更多查看译文
关键词
Molecular dynamics,Atomic cluster expansion,Fe-Co Alloy,Density functional theory,Phase transition,Force field
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