Park, S. M. Yoon, H. G. Lee, D. B. Choi, Jun Woo Kwon, Hee Young Won, C. 2024-01-19T12:02:17Z 2024-01-19T12:02:17Z 2022-06-17 2022-05 2045-2322 https://pubs.kist.re.kr/handle/201004/115197 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. English Nature Publishing Group Optimization of physical quantities in the autoencoder latent space Article 10.1038/s41598-022-13007-5 1 Scientific Reports, v.12, no.1 Scientific Reports 12 1 Y scie scopus 000805674000036 Multidisciplinary Sciences Science & Technology - Other Topics Article SELF-CONSISTENT DESIGN