Magnetic films, in which magnetic vortex textures - skyrmions appear because of competition between the direct Heisenberg exchange and the Dzyaloshinskii-Moriya interaction, were studied using the Monte-Carlo simulation technique. The conditions for the nucleation and stable existence of magnetic skyrmions in magnetic films in the frame of the classical Heisenberg model were considered in the paper. The process of nucleation of skyrmions with increasing of the external magnetic field was studied, various phases into which the Heisenberg spin system passes were recognized. A phase diagram was plotted: it shows the behavior of the system at the constant value of temperature depending on values of an external magnetic field and Dzyaloshinskii-Moriya interaction.
В данной работе с помощью алгоритма Метрополиса авторами были изучены магнитные системы, в которых из-за конкуренции между прямым гейзенберговским обменом и взаимодействием Дзялошинского-Мория возникают магнитные вихревые структуры-скирмионы. В статье рассматриваются условия зарождения и стабильного существования магнитных скирмионов в двумерных магнитных пленках в рамках классической модели Гейзенберга. Изучена термическая стабильность скирмионов в магнитной пленке. Были рассмотрены процессы формирования различных состояний в изучаемой системе при варьировании величины внешнего магнитного поля, выделены различные фазы, в которые переходит система спинов Гейзенберга. Было выделено семь фаз: парамагнитная, спиральная, лабиринтная, спираль-скирмионная, скирмионная, скирмион-ферромагнитная и ферромагнитная фазы, подробный анализ конфигураций которых приводится в статье. Построены две фазовые диаграммы: на первой показано поведение системы при постоянном D в зависимости от величин внешнего магнитного поля и температуры: (T, B), на второй-изменение конфигураций системы при постоянной температуре T в зависимости от величины взаимодействия Дзялошинского-Мории и внешнего магнитного поля: (D, B). Полученные в ходе численных экспериментов данные будут использованы в дальнейших исследованиях при определении модельных параметров системы для формирования стабильного скирмионного состояния и разработки методов контроля скирмионов в магнитной пленке. Ключевые слова: магнитный скирмион, модель Гейзенберга, алгоритм Метрополиса, фазовая диаграмма, высокопроизводительные вычисления Работа выполнена при финансовой поддержке Министерства науки и высшего образования, государственное задание № 075-00400-19-01.
We studied several types of flat lattices with direct exchange and Dzyaloshinskii-Moriya interaction between spins: a honeycomb lattice with 3 nearest neighbours (NN), a square lattice with 4 NN and a hexagonal or triangular lattice with 6 NN. For the analysis of data obtained during the Monte Carlo simulation, a convolutional neural network was used for the recognition of different phases of the spin system which was dependent on simulation parameters such as DMI and external magnetic field (Hz). Based on these data, the phase diagrams (Hz, D) for the different lattices were plotted. The various states of the systems under observation were visualised and the boundaries between the different phases were defined as a spiral, a skyrmion and others. The data from the numerical experiments will be used in further studies to determine the model parameters of the systems for the formation of a stable skyrmion state and the development of methods for controlling skyrmions in a magnetic film.
The authors describe a method for determining the critical point of a second order phase transitions using a convolutional neural network based on the Ising model on a square lattice. Data for training and analysis were obtained using Monte Carlo simulations. The neural network was trained on the data corresponding to the low-temperature phase, that is a ferromagnetic one and high-temperature phase, that is a paramagnetic one, respectively. After training, the neural network analyzed input data from the entire temperature range: from 0.1 to 5.0 (in dimensionless units J) and determined the Curie point Tc.
The authors describe a method for determining the critical point of a second-order phase transitions using a convolutional neural network based on the Ising model on a square lattice. Data for training were obtained using Metropolis algorithm for different temperatures. The neural network was trained on the data corresponding to the low-temperature phase, that is a ferromagnetic one and high-temperature phase, that is a paramagnetic one, respectively. After training, the neural network analyzed input data from the entire temperature range: from 0.1 to 5.0 (in dimensionless units) and determined (the Curie temperature T_c). The accuracy of the obtained results was estimated relative to the Onsager solution for a flat lattice of Ising spins.
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