Magnetic State Generation using Hamiltonian Guided Variational Autoencoder with Spin Structure Stabilization

Yoon²,

Lee³

et al. 2022

We propose a strategy for optimizing physical quantities based on exploring in the latent space of a variational autoencoder (VAE). We train a VAE model using various spin configurations formed on a two-dimensional chiral magnetic system. Three optimization algorithms are used to explore the latent space of the trained VAE. The first algorithm, the single-code modification algorithm, is designed for improving the local energetic stability of spin configurations to generate physically plausible spin states. The other two algorithms, the genetic algorithm and the stochastic algorithm, aim to optimize the global physical quantities, such as topological index, magnetization, energy, and directional correlation. The advantage of our method is that various optimization algorithms can be applied in the latent space containing the abstracted representation constructed by the trained VAE model. Our method based on latent space exploration is utilized for efficient physical quantity optimization.

Section: Resultsmentioning

confidence: 99%

Section: Strategymentioning

confidence: 88%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Optimization of physical quantities in the autoencoder latent space

Yoon²,

Lee³

et al. 2022

“…Recently, an innovative ground state estimation method based on a novel deep generative model, Energy-minimization variational autoencoder (E-VAE), was devised 14 . The E-VAE model is composed of the encoder and decoder network structures similar to the original variational autoencoder (VAE) model 15 , and the encoder part compresses input data into a new representation in the reduced data dimension.…”

Section: Introductionmentioning

confidence: 99%

Searching for the ground state of complex spin-ice systems using deep learning techniques

Kwon

Yoon

et al. 2022

Searching for the ground state of a given system is one of the most fundamental and classical questions in scientific research fields. However, when the system is complex and large, it often becomes an intractable problem; there is essentially no possibility of finding a global energy minimum state with reasonable computational resources. Recently, a novel method based on deep learning techniques was devised as an innovative optimization method to estimate the ground state. We apply this method to one of the most complicated spin-ice systems, aperiodic Penrose P3 patterns. From the results, we discover new configurations of topologically induced emergent frustrated spins, different from those previously known. Additionally, a candidate of the ground state for a still unexplored type of Penrose P3 spin-ice system is first proposed through this study. We anticipate that the capabilities of the deep learning techniques will not only improve our understanding on the physical properties of artificial spin-ice systems, but also bring about significant advances in a wide range of scientific research fields requiring computational approaches for optimization.

“…In addition, deep learning techniques have achieved remarkable success in modeling physical data. In particular, it has been intensively applied in the field of magnetism research to investigate various properties of unique magnetic domain structures appearing in low dimensions, estimate Hamiltonian parameters of magnetic complex structures from experimental observation 7 – 9 and generate ground states in various systems 10 – 12 .…”

Section: Introductionmentioning

confidence: 99%

Estimating the effective fields of spin configurations using a deep learning technique

Lee

Yoon

et al. 2021

The properties of complicated magnetic domain structures induced by various spin–spin interactions in magnetic systems have been extensively investigated in recent years. To understand the statistical and dynamic properties of complex magnetic structures, it is crucial to obtain information on the effective field distribution over the structure, which is not directly provided by magnetization. In this study, we use a deep learning technique to estimate the effective fields of spin configurations. We construct a deep neural network and train it with spin configuration datasets generated by Monte Carlo simulation. We show that the trained network can successfully estimate the magnetic effective field even though we do not offer explicit Hamiltonian parameter values. The estimated effective field information is highly applicable; it is utilized to reduce noise, correct defects in the magnetization data, generate spin configurations, estimate external field responses, and interpret experimental images.