Monte Carlo study of magnetic nanoparticles adsorbed on halloysite Al2Si2O5(OH)4Al_2Si_2O_5(OH)_4 nanotubes

We study properties of magnetic nanoparticles adsorbed on the halloysite surface. For that a distinct magnetic Hamiltonian with random distribution of spins on a cylindrical surface was solved by using a nonequilibrium Monte Carlo method. The parameters for our simulations: anisotropy constant, nanoparticle size distribution, saturated magnetization and geometrical parameters of the halloysite template were taken from recent experiments. We calculate the hysteresis loops and temperature dependence of the zero field cooling (ZFC) susceptibility, which maximum determines the blocking temperature. It is shown that the dipole-dipole interaction between nanoparticles moderately increases the blocking temperature and weakly increases the coercive force. The obtained hysteresis loops (e.g., the value of the coercive force) for Ni nanoparticles are in reasonable agreement with the experimental data. We also discuss the sensitivity of the hysteresis loops and ZFC susceptibilities to the change of anisotropy and dipole-dipole interaction, as well as the 3d-shell occupation of the metallic nanoparticles; in particular we predict larger coercive force for Fe, than for Ni nanoparticles.Comment: 10 pages, 12 figure

Bimeron nanoconfined design

We report on the stabilization of the topological bimeron excitations in confined geometries. The Monte Carlo simulations for a ferromagnet with a strong Dzyaloshinskii-Moriya interaction revealed the formation of a mixed skyrmion-bimeron phase. The vacancy grid created in the spin lattice drastically changes the picture of the topological excitations and allows one to choose between the formation of a pure bimeron and skyrmion lattice. We found that the rhombic plaquette provides a natural environment for stabilization of the bimeron excitations. Such a rhombic geometry can protect the topological state even in the absence of the magnetic field.Comment: 5 pages, 7 figure

Profile approach for recognition of three-dimensional magnetic structures

We propose an approach for low-dimensional visualisation and classification of complex topological magnetic structures formed in magnetic materials. Within the approach one converts a three-dimensional magnetic configuration to a vector containing the only components of the spins that are parallel to the z axis. The next crucial step is to sort the vector elements in ascending or descending order. Having visualized profiles of the sorted spin vectors one can distinguish configurations belonging to different phases even with the same total magnetization. For instance, spin spiral and paramagnetic states with zero total magnetic moment can be easily identified. Being combined with a simplest neural network our profile approach provides a very accurate phase classification for three-dimensional magnets characterized by complex multispiral states even in the critical areas close to phases transitions. By the example of the skyrmionic configurations we show that profile approach can be used to separate the states belonging to the same phase

Variational optimization of tensor-network states with the honeycomb-lattice corner transfer matrix

We develop a method of variational optimization of the infinite projected entangled pair states on the honeycomb lattice. The method is based on the automatic differentiation of the honeycomb lattice corner transfer matrix renormalization group. We apply the approach to the antiferromagnetic Heisenberg spin-1/2 model on the honeycomb lattice. The developed formalism gives quantitatively accurate results for the main physical observables and has a necessary potential for further extensions

Supervised learning magnetic skyrmion phases

We propose and apply simple machine learning approaches for recognition and classification of complex non-collinear magnetic structures in two-dimensional materials. The first approach is based on the implementation of the single-hidden-layer neural network that only relies on the z projections of the spins. In this setup one needs a limited set of magnetic configurations to distinguish ferromag- netic, skyrmion and spin spiral phases, as well as their different combinations in transitional areas of the phase diagram. The network trained on the configurations for square-lattice Heisenberg model with Dzyaloshinskii-Moriya interaction can classify the magnetic structures obtained from Monte Carlo calculations for triangular lattice and vice versa. The second approach we apply, a minimum distance method performs a fast and cheap classification in cases when a particular configuration is to be assigned to only one magnetic phase. The methods we propose are also easy to use for analysis of the numerous experimental data collected with spin-polarized scanning tunneling microscopy and Lorentz transmission electron microscopy experiments.Comment: 9 pages, 14 figures. Accepted for publication in Physical Review

About a contactless transmission of 10 keV electrons through tapering microchannels

The possibility of increasing the current density of a beam of fast electrons passes through glass cone-shaped channels was demonstrated in. But the fraction of the electrons that passed through the conical channels without loss of the initial energy was not cleared up. Measurements of X-ray spectra generated by transmitted electrons in copper target mounted in vicinity of capillary output were performed for a detailed study of the contactless passage of 10 keV electrons through conical capillarie