Phase prediction method for pattern formation in time-dependent Ginzburg-Landau dynamics for kinetic Ising model without a priori assumptions of domain patterns
Phase prediction method for pattern formation in time-dependent Ginzburg-Landau dynamics for kinetic Ising model without a priori assumptions of domain patterns
复制标题
动态 Ising 模型的瞬态 Ginzburg-Landau 动力学中模式形成的相位预测方法，无需域模式的先验假设
DOI：
10.1103/physrevb.103.094408
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发表时间：
2021
影响因子：
3.7
通讯作者：
and I. Akai
中科院分区：
文献类型：
--
作者：
Anzaki;R.;S. Ito;H. Nagao;M. Mizumaki;M. Okada;and I. Akai
本文提出了一种在含时的Ginzburg-Landau动力学模型下，具有偶极-偶极相互作用的二维动力学Ising模型中斑图形成的相位预测方法。考虑材料厚度的影响，假设沿着磁化轴的均匀性，该模型对应于具有长程排斥相互作用的薄磁性材料。我们制定了一个理论基础，以了解在平衡状态下的线性和非线性力之间的平衡的平衡方程的磁畴图案的形成的材料参数的影响。此外，我们开发了一种方法，用于预测在平衡状态下实现的系统与给定的初始参数的动态演化后的相位。对于模型，使用受限相空间近似[Anzaki，Ann. Phys.（NY）353，107（2015）APNYA 60003 -491610.1016/j.aop.2014.11.004]来近似分析上困难的三阶项。虽然该方法与实际数值结果并不完全一致，但它没有任意的参数和函数来调整预测。换句话说，它是一种对领域模式没有优先假设的方法。
We propose a phase prediction method for pattern formation in a two-dimensional kinetic Ising model with dipole-dipole interactions under the time-dependent Ginzburg-Landau dynamics. Considering the effects of the material thickness by assuming uniformness along the magnetization axis, the model corresponds to thin magnetic materials with long-range repulsive interactions. We formulate a theoretical basis to understand the effects of the material parameters on the formation of the magnetic domain patterns in terms of the equilibrium equations governing the balance between the linear and nonlinear forces in the equilibrium state. Further, we develop a method for predicting the phase in the equilibrium state achieved after the dynamical evolution of a system with given initial parameters. The analytically hard third-order term is approximated using the restricted phase-space approximation [Anzaki , Ann. Phys. (NY) 353, 107 (2015)APNYA60003-491610.1016/j.aop.2014.11.004] for themodels. Although the proposed method does not have perfect concordance with the actual numerical results, it has no arbitrary parameters and functions totunethe prediction. In other words, it is a method with noa prioriassumptions of the domain patterns.