Machine Learning Magnetic Parameters from Spin Configurations.,Advanced Science

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Machine Learning Magnetic Parameters from Spin Configurations.
Advanced Science ( IF 14.3 ) Pub Date : 2020-07-01 , DOI: 10.1002/advs.202000566
Dingchen Wang ₁ , Songrui Wei ₂ , Anran Yuan ₃ , Fanghua Tian ₁ , Kaiyan Cao ₁ , Qizhong Zhao ₁ , Yin Zhang ₁ , Chao Zhou ₁ , Xiaoping Song ₁ , Dezhen Xue ₁ , Sen Yang ₁

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Hamiltonian parameters estimation is crucial in condensed matter physics, but is time‐ and cost‐consuming. High‐resolution images provide detailed information of underlying physics, but extracting Hamiltonian parameters from them is difficult due to the huge Hilbert space. Here, a protocol for Hamiltonian parameters estimation from images based on a machine learning (ML) architecture is provided. It consists in learning a mapping between spin configurations and Hamiltonian parameters from a small amount of simulated images, applying the trained ML model to a single unexplored experimental image to estimate its key parameters, and predicting the corresponding materials properties by a physical model. The efficiency of the approach is demonstrated by reproducing the same spin configuration as the experimental one and predicting the coercive field, the saturation field, and even the volume of the experiment specimen accurately. The proposed approach paves a way to achieve a stable and efficient parameters estimation.

中文翻译：

来自自旋配置的机器学习磁性参数。

哈密顿参数估计在凝聚态物理中至关重要，但既费时又费钱。高分辨率图像提供了底层物理的详细信息，但由于希尔伯特空间巨大，从中提取哈密顿参数很困难。这里，提供了一种基于机器学习（ML）架构的图像哈密顿参数估计协议。它包括从少量模拟图像中学习自旋构型和哈密顿参数之间的映射，将经过训练的机器学习模型应用于单个未探索的实验图像以估计其关键参数，并通过物理模型预测相应的材料特性。通过重现与实验相同的自旋构型并准确预测矫顽场、饱和场，甚至实验样本的体积，证明了该方法的效率。所提出的方法为实现稳定有效的参数估计铺平了道路。

更新日期：2020-08-19

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