Yoona, H. G. Leea, D. B. Parka, S. M. Choib, J. W. Kwon, H. Y. Won, C. 2024-08-29T06:30:05Z 2024-08-29T06:30:05Z 2024-08-29 2024-12 0010-4655 https://pubs.kist.re.kr/handle/201004/150521 The phase transition phenomenon is an important research topic in various physical studies. However, it is difficult to define the order parameters in many complex systems involving self-organized structures. We propose a method to define order parameters using a variational autoencoder network. To demonstrate these capabilities, we trained a deep learning network with a dataset composed of spin configurations in a chiral magnetic system at various temperatures. It removes thermal fluctuations from the input data and leaves the remaining structural information with a spin magnitude. We define an order parameter with magnitude of output spins and compare the results with those of conventional analysis. The comparison indicates similar results. Using the order parameter, the thermal properties of the chiral magnetic system were investigated by varying the physical parameters and data size. English Elsevier BV Melting phenomena of self-organized magnetic structures investigated by variational autoencoder Article 10.1016/j.cpc.2024.109329 1 Computer Physics Communications, v.305 Computer Physics Communications 305 Y scie scopus 001291732100001 2-s2.0-85200894392 Computer Science, Interdisciplinary Applications Physics, Mathematical Computer Science Physics Article PHASE-TRANSITIONS FERROMAGNETISM Spontaneous symmetry breaking Phase transition Order parameter Critical temperature Magnetism Deep learning