从自旋配置中机器学习磁性参数
Machine learning magnetic parameters from spin configurations
摘要：
在凝聚态物理中，哈密顿量参数估计是一个非常重要的问题，但在资源消耗方面，它是一个耗时耗力的问题。随着观测技术的进步，获得了具有更详细信息的高分辨率图像，这些图像可以作为机器学习（ML）算法的输入，以提取哈密顿参数。然而，标记图像的数量是相当有限的。在这里，我们提供了一个基于机器学习架构的哈密顿参数估计协议，该协议在少量模拟图像上进行训练，并应用于实验自旋配置图像。在输入图像上滑动窗口扩大了训练图像的数量，因此我们可以在一个小的模拟图像数据集上训练好神经网络，这些模拟图像是使用与实验相同的外部条件，如温度和磁场自适应生成的。将神经网络应用于实验图像，有效地估计了磁参数。我们证明了成功的估计，通过复制相同的配置，从模拟和预测的磁滞回线准确。我们的方法铺平了道路，一个稳定的和一般的参数估计。
英文摘要：
Hamiltonian parameter estimation is crucial in condensed matter physics, but time and cost consuming in terms of resources used. With advances in observation techniques, high-resolution images with more detailed information are obtained, which can serve as an input to machine learning (ML) algorithms to extract Hamiltonian parameters. However, the number of labeled images is rather limited. Here, we provide a protocol for Hamiltonian parameter estimation based on a machine learning architecture, which is trained on a small amount of simulated images and applied to experimental spin configuration images. Sliding windows on the input images enlarges the number of training images; therefore we can train well a neural network on a small dataset of simulated images which are generated adaptively using the same external conditions such as temperature and magnetic field as the experiment. The neural network is applied to the experimental image and estimates magnetic parameters efficiently. We demonstrate the success of the estimation by reproducing the same configuration from simulation and predict a hysteresis loop accurately. Our approach paves a way to a stable and general parameter estimation.
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。
