An innovative magnetic state generator using machine learning techniques

Title
An innovative magnetic state generator using machine learning techniques
Authors
최준우권희영이찬기N. J. KimH. G. YoonC. Won
Keywords
machine learning; magnetic domains
Issue Date
2019-11
Publisher
Scientific Reports
Citation
VOL 9-16706-7
Abstract
We propose a new efficient algorithm to simulate magnetic structures numerically. It contains a generative model using a complex-valued neural network to generate k-space information. The output information is hermitized and transformed into real-space spin configurations through an inverse fast Fourier transform. The Adam version of stochastic gradient descent is used to minimize the magnetic energy, which is the cost of our algorithm. The algorithm provides the proper ground spin configurations with outstanding performance. In model cases, the algorithm was successfully applied to solve the spin configurations of magnetic chiral structures. The results also showed that a magnetic long-range order could be obtained regardless of the total simulation system size.
URI
http://pubs.kist.re.kr/handle/201004/70416
ISSN
2045-2322
Appears in Collections:
KIST Publication > Article
Files in This Item:
There are no files associated with this item.
Export
RIS (EndNote)
XLS (Excel)
XML


qrcode

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

BROWSE