Wang Dingchen, Wei Songrui, Yuan Anran, Tian Fanghua, Cao Kaiyan, Zhao Qizhong, Zhang Yin, Zhou Chao, Song Xiaoping, Xue Dezhen, Yang Sen
MOE Key Laboratory for Nonequilibrium Synthesis and Modulation of Condensed Matter School of Science State Key Laboratory for Mechanical Behavior of Materials Xi'an Jiaotong University Xi'an 710049 China.
Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province College of Optoelectronic Engineering Shenzhen University Shenzhen 518060 China.
Adv Sci (Weinh). 2020 Jul 1;7(16):2000566. doi: 10.1002/advs.202000566. eCollection 2020 Aug.
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.
哈密顿量参数估计在凝聚态物理中至关重要，但耗时且成本高昂。高分辨率图像提供了潜在物理的详细信息，但由于巨大的希尔伯特空间，从这些图像中提取哈密顿量参数很困难。在此，提供了一种基于机器学习（ML）架构从图像估计哈密顿量参数的方法。它包括从少量模拟图像中学习自旋构型与哈密顿量参数之间的映射，将训练好的ML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。通过再现与实验相同的自旋构型并准确预测矫顽场、饱和场甚至实验样品的体积，证明了该方法的有效性。所提出的方法为实现稳定高效的参数估计铺平了道路。
