Magnetic Hamiltonian parameter estimation using deep learning techniques

Title
Magnetic Hamiltonian parameter estimation using deep learning techniques
Authors
최준우권희영H. G. YoonC. LeeG. ChenK. LiuA. K. SchmidY. Z. WuC. Won
Keywords
deep learning; Magnetic Hamiltonian
Issue Date
2020-09
Publisher
Science Advances
Citation
VOL 6, eabb08720872
Abstract
Understanding spin textures in magnetic systems is extremely important to the spintronics and it is vital to extrapolate the magnetic Hamiltonian parameters through the experimentally determined spin. It can provide a better complementary link between theories and experimental results. We demonstrate deep learning can quantify the magnetic Hamiltonian from magnetic domain images. To train the deep neural network, we generated domain configurations with Monte Carlo method. The errors from the estimations was analyzed with statistical methods and confirmed the network was successfully trained to relate the Hamiltonian parameters with magnetic structure characteristics. The network was applied to estimate experimentally observed domain images. The results are consistent with the reported results, which verifies the effectiveness of our methods. On the basis of our study, we anticipate that the deep learning techniques make a bridge to connect the experimental and theoretical approaches not only in magnetism but also throughout any scientific research.
URI
http://pubs.kist.re.kr/handle/201004/71907
ISSN
2375-2548
Appears in Collections:
KIST Publication > Article
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