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arXiv 2212.09796cond-mat.str-el
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机器学习辅助推导金属磁体有效低能模型

Machine learning assisted derivation of effective low energy models for metallic magnets

Vikram Sharma, Zhentao Wang, Cristian D. Batista

AI总结:

本文提出一种机器学习辅助协议,从近藤晶格模型中提取有效双自旋和四自旋相互作用,所得最小自旋模型能高效复现相图并揭示磁场诱导斯格明子晶体相的起源。

摘要:

我们考虑从具有经典局域磁矩的近藤晶格模型(KLM)中提取有效低能自旋模型的问题。有效自旋-自旋相互作用对近藤交换耦合$J$的非解析依赖排除了在零温下使用二阶Ruderman-Kittel-Kasuya-Yosida(RKKY)相互作用之外的微扰理论的可能性。在此,我们引入一种机器学习(ML)辅助协议,通过对原始KLM中的传导电子进行积分来提取有效的双自旋和四自旋相互作用。所得的有效自旋模型再现了原始KLM作为磁场和易轴各向异性函数所获得的相图,并揭示了导致磁场诱导斯格明子晶体相的有效四自旋相互作用。此外,这一最小自旋模型能够以远低于原始KLM的数值成本高效计算静态和动力学性质。对完全极化相中动力学自旋结构因子的比较表明,尽管该信息未包含在训练数据集中,有效模型与原始KLM计算得到的磁振子色散仍具有良好的一致性。

英文摘要:

We consider the problem of extracting an effective low-energy spin model from a Kondo Lattice Model (KLM) with classical localized moments. The non-analytic dependence of the effective spin-spin interactions on the Kondo exchange $J$ excludes the possibility of using perturbation theory beyond the second order Ruderman-Kittel-Kasuya-Yosida (RKKY) interaction at zero temperature. Here we introduce a Machine Learning (ML) assisted protocol to extract effective two- and four-spin interactions by integrating out the conduction electrons of the original KLM. The resulting effective spin model reproduces the phase diagram obtained with the original KLM as a function of magnetic field and easy-axis anisotropy and reveals the effective four-spin interactions that are responsible for the field induced skyrmion crystal phase. Moreover, this minimal spin model enables an efficient computation of static and dynamical properties with a much lower numerical cost relative to the original KLM. A comparison of the dynamical spin structure factor in the fully polarized phase computed with the effective model and the original KLM reveals a good agreement for the magnon dispersion despite the fact that this information was not included in the training data set.

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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