论文标题
用于构建Skyrmion系统详细相图的机器学习技术
Machine learning techniques to construct detailed phase diagrams for skyrmion systems
论文作者
论文摘要
最近，对机器学习（ML）技术的应用（ML）技术引起了浓缩物理学的各种问题的兴趣。在这方面，特别重要的是物质的简单和复杂阶段的表征。在这里，我们使用一种ML方法来构建结合铁磁交换和dzyaloshinskii-Moriya（DM）相互作用的众所周知的自旋模型的完整相图，其中拓扑相位出现。在低温下，该系统通过磁场从螺旋相调整到天空晶体。然而，热波动诱导两种类型的中间相，bimerons和skyrmion气体，它们不像螺旋形或天际晶体那样容易确定。我们诉诸于大规模的蒙特卡洛模拟以获得低温自旋构型，并训练卷积神经网络（CNN），仅在DM耦合的特定值上拍摄快照，以在不同的相之间进行分类，重点是中间和复杂的拓扑纹理。然后，我们将CNN应用于较高的温度构型和其他DM值，以构建详细的磁场 - 温度相图，从而实现出色的结果。我们讨论了包括无序的顺磁性阶段以获得相边界的重要性，最后，我们将方法与其他ML算法进行了比较。
Recently, there has been an increased interest in the application of machine learning (ML) techniques to a variety of problems in condensed matter physics. In this regard, of particular significance is the characterization of simple and complex phases of matter. Here, we use a ML approach to construct the full phase diagram of a well known spin model combining ferromagnetic exchange and Dzyaloshinskii-Moriya (DM) interactions where topological phases emerge. At low temperatures, the system is tuned from a spiral phase to a skyrmion crystal by a magnetic field. However, thermal fluctuations induce two types of intermediate phases, bimerons and skyrmion gas, which are not as easily determined as spirals or skyrmion crystals. We resort to large scale Monte Carlo simulations to obtain low temperature spin configurations, and train a convolutional neural network (CNN), taking only snapshots at specific values of the DM couplings, to classify between the different phases, focusing on the intermediate and intricate topological textures. We then apply the CNN to higher temperature configurations and to other DM values, to construct a detailed magnetic field-temperature phase diagram, achieving outstanding results. We discuss the importance of including the disordered paramagnetic phases in order to get the phase boundaries, and finally, we compare our approach with other ML algorithms.
