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Area of Science:

  • Condensed Matter Physics
  • Computational Physics
  • Materials Science

Background:

  • Chiral magnetic systems exhibit complex spin configurations.
  • Optimizing physical quantities in these systems is crucial for understanding their properties.
  • Traditional optimization methods can be computationally intensive.

Purpose of the Study:

  • To develop an efficient strategy for optimizing physical quantities in chiral magnetic systems.
  • To leverage the latent space of a variational autoencoder (VAE) for optimization.
  • To explore the application of different optimization algorithms within the VAE's latent space.

Main Methods:

  • Training a VAE model on spin configurations from a 2D chiral magnetic system.
  • Employing three optimization algorithms: single-code modification, genetic algorithm, and stochastic algorithm.
  • Exploring the latent space of the trained VAE to identify optimal physical quantities.

Main Results:

  • The VAE successfully learns an abstracted representation of spin configurations.
  • The single-code modification algorithm enhances local energetic stability for plausible spin states.
  • The genetic and stochastic algorithms effectively optimize global quantities like topological index and magnetization.

Conclusions:

  • Latent space exploration of VAEs provides an efficient framework for physical quantity optimization.
  • This method allows for the flexible application of diverse optimization algorithms.
  • The proposed strategy offers a novel approach to studying complex magnetic systems.