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arXiv 2407.01159cond-mat.str-el
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通过逆哈密顿量设计探索拓扑自旋序:一种稳定方形斯格明子晶格的新机制

Exploring topological spin order by inverse Hamiltonian design: A new stabilization mechanism for square skyrmion crystals

Kazuki Okigami, Satoru Hayami

AI总结:

提出基于逆哈密顿量设计和机器学习的框架,通过优化长程交换相互作用实现了无需传统先决条件的方形斯格明子晶格,为构建有效自旋模型和理解拓扑自旋序稳定机制提供了普适方法。

摘要:

我们提出了一种基于逆哈密顿量设计构建实空间自旋模型的框架。该方法通过优化相互作用参数,为实现非常规拓扑自旋结构提供了高效途径。为证明其有效性,我们展示了调节长程交换相互作用能够产生方形斯格明子晶格,即使不存在先前被认为是其稳定化先决条件的因素,如 Dzyaloshinskii-Moriya 相互作用、多自旋相互作用以及键依赖的磁各向异性。此外,我们通过对该方法获得的参数集进行分类,阐明了方形斯格明子晶格出现的本质。由于本框架采用了机器学习技术,可普遍应用于任何磁性系统而不受晶格结构限制,因此它能有效构建有效自旋模型并理解非常规拓扑自旋序的稳定机制。

英文摘要:

We propose a framework to construct a real-space spin model based on the inverse Hamiltonian design. The method provides an efficient way of realizing unconventional topological spin textures by optimizing the interaction parameters. In order to demonstrate its usefulness, we show that the tuning of the long-range exchange interactions can give rise to a square skyrmion crystal even without factors that have been previously identified as prerequisites for its stabilization, such as Dzyaloshinskii-Moriya interaction, multi-spin interaction, and bond-dependent magnetic anisotropy. Moreover, we elucidate the essence for the emergence of the square skyrmion crystal by classifying the parameter sets we get by the method. Since the present framework by adopting machine learning techniques can be universally applied to any magnetic system irrespective of lattice structures, it serves as the efficient construction of an effective spin model and the understanding of the stabilization mechanisms for unconventional topological spin orders.

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。

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