Identifying magnetic antiskyrmions while they form with convolutional neural networks
Identifying magnetic antiskyrmions while they form with convolutional neural networks
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DOI：
10.1016/j.jmmm.2022.169806
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发表时间：
2022-05
影响因子：
2.7
通讯作者：
Jack Y. Araz;J. C. Criado;M. Spannowsky
中科院分区：
文献类型：
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作者：
Jack Y. Araz;J. C. Criado;M. Spannowsky
近年来，手性磁体吸引了大量的研究兴趣，因为它们支持各种拓扑缺陷，如skyrmions和bimerons，并允许通过几种技术对它们进行观察和操作。它们在自旋电子学领域也有广泛的应用，特别是在开发内存存储设备的新技术方面。然而，这些实验和理论研究中产生的大量数据需要足够的工具，其中机器学习是至关重要的。我们使用卷积神经网络（CNN）来识别手性磁体的热力学相的相关特征，包括（反）skyrmions, bimerons，螺旋态和铁磁态。我们使用了一个灵活的多标签分类框架，可以正确地分类不同特征和阶段混合的状态。然后，我们训练CNN从晶格蒙特卡罗模拟的中间状态快照中预测最终状态的特征。经过训练的模型可以在地层过程的早期可靠地识别不同阶段。因此，CNN可以显著加快3D材料的大规模模拟，而这一直是定量研究的瓶颈。此外，该方法还可以应用于手性磁体真实图像中混合状态和新特征的识别。
Chiral magnets have attracted a large amount of research interest in recent years because they support a variety of topological defects, such as skyrmions and bimerons, and allow for their observation and manipulation through several techniques. They also have a wide range of applications in the field of spintronics, particularly in developing new technologies for memory storage devices. However, the vast amount of data generated in these experimental and theoretical studies requires adequate tools, among which machine learning is crucial. We use a Convolutional Neural Network (CNN) to identify the relevant features in the thermodynamical phases of chiral magnets, including (anti-)skyrmions, bimerons, and helical and ferromagnetic states. We use a flexible multi-label classification framework that can correctly classify states in which different features and phases are mixed. We then train the CNN to predict the features of the final state from snapshots of intermediate states of a lattice Monte Carlo simulation. The trained model allows identifying the different phases reliably and early in the formation process. Thus, the CNN can significantly speed up the large-scale simulations for 3D materials that have been the bottleneck for quantitative studies so far. Moreover, this approach can be applied to the identification of mixed states and emerging features in real-world images of chiral magnets.