arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2403.08587cond-mat.str-el
收藏

一种改进的粒子群优化算法及其在Hubbard模型中搜索新的磁性基态的应用

An improved particle swarm optimization algorithm and its application to search for new magnetic ground states in the Hubbard model

Ze Ruan, Xiu-Cai Jiang, Ze-Yi Song, Yu-Zhong Zhang

AI总结:

提出改进的粒子群优化算法,在基准函数上验证其优越性,并应用于Hubbard模型,发现两种新的磁性基态,且确认其非有限尺寸效应。

摘要:

提出了一种改进的粒子群优化算法,并在两个典型基准函数上测试了其相对于标准粒子群优化算法的优越性。利用该算法,基于平均场近似,在有限尺寸的实空间方格上搜索Hubbard模型的磁性基态,发现了两种新的磁性态,即双条纹型反铁磁态和三重反铁磁态。我们进一步在热力学极限下进行平均场计算,以确认这两种新的磁性态并非有限尺寸效应的结果,同时还给出了双条纹型反铁磁态的性质。

英文摘要:

An improved particle swarm optimization algorithm is proposed and its superiority over standard particle swarm optimization algorithm is tested on two typical benchmark functions. By employing this algorithm to search for the magnetic ground states of the Hubbard model on the real-space square lattice with finite size based on the mean-field approximation, two new magnetic states, namely the double striped-type antiferromagnetic state and the triple antiferromagnetic state, are found. We further perform mean-field calculations in the thermodynamical limit to confirm that these two new magnetic states are not a result of a finite-size effect, where the properties of the double striped-type antiferromagnetic state are also presented.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑