扭转范德华磁体中哈密顿参数估计和磁域图像生成的深度学习方法

IF 6.3 2区 物理与天体物理 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Woo Seok Lee, Taegeun Song and Kyoung-Min Kim
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引用次数: 0

摘要

范德华磁体扭转工程的应用开辟了二维磁学领域的新领域,产生了独特的磁畴结构。尽管引入了大量理论方法,但由于这些系统相关的磁性哈密顿的复杂性,在精度或效率方面仍然存在限制。在本研究中,我们引入了一种深度学习方法来应对这些挑战。利用定制的全连接网络,我们开发了两种深度神经网络内核,有助于对扭曲范德华磁体进行高效可靠的分析。我们的回归模型善于从原子自旋模拟生成的磁畴图像中估计扭曲双层 CrI3 的磁性哈密顿参数。生成模型 "擅长根据提供的磁参数生成精确的磁畴图像。这些模型的训练网络经过了全面的验证,包括统计误差分析和对噪声注入的鲁棒性评估。这些进展不仅扩展了深度学习方法在扭曲范德华磁体中的适用性,还简化了未来对这些令人着迷但却鲜为人知的系统的研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deep learning methods for Hamiltonian parameter estimation and magnetic domain image generation in twisted van der Waals magnets
The application of twist engineering in van der Waals magnets has opened new frontiers in the field of two-dimensional magnetism, yielding distinctive magnetic domain structures. Despite the introduction of numerous theoretical methods, limitations persist in terms of accuracy or efficiency due to the complex nature of the magnetic Hamiltonians pertinent to these systems. In this study, we introduce a deep-learning approach to tackle these challenges. Utilizing customized, fully connected networks, we develop two deep-neural-network kernels that facilitate efficient and reliable analysis of twisted van der Waals magnets. Our regression model is adept at estimating the magnetic Hamiltonian parameters of twisted bilayer CrI3 from its magnetic domain images generated through atomistic spin simulations. The ‘generative model’ excels in producing precise magnetic domain images from the provided magnetic parameters. The trained networks for these models undergo thorough validation, including statistical error analysis and assessment of robustness against noisy injections. These advancements not only extend the applicability of deep-learning methods to twisted van der Waals magnets but also streamline future investigations into these captivating yet poorly understood systems.
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来源期刊
Machine Learning Science and Technology
Machine Learning Science and Technology Computer Science-Artificial Intelligence
CiteScore
9.10
自引率
4.40%
发文量
86
审稿时长
5 weeks
期刊介绍: Machine Learning Science and Technology is a multidisciplinary open access journal that bridges the application of machine learning across the sciences with advances in machine learning methods and theory as motivated by physical insights. Specifically, articles must fall into one of the following categories: advance the state of machine learning-driven applications in the sciences or make conceptual, methodological or theoretical advances in machine learning with applications to, inspiration from, or motivated by scientific problems.
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