177 research outputs found
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
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
Quantifying spatio-temporal patterns in classical and quantum systems out of equilibrium
A rich variety of non-equilibrium dynamical phenomena and processes
unambiguously calls for the development of general numerical techniques to
probe and estimate a complex interplay between spatial and temporal degrees of
freedom in many-body systems of completely different nature. In this work we
provide a solution to this problem by adopting a structural complexity measure
to quantify spatio-temporal patterns in the time-dependent digital
representation of a system. On the basis of very limited amount of data our
approach allows to distinguish different dynamical regimes and define critical
parameters in both classical and quantum systems. By the example of the
discrete time crystal realized in non-equilibrium quantum systems we provide a
complete low-level characterization of this nontrivial dynamical phase with
only processing bitstrings, which can be considered as a valuable alternative
to previous studies based on the calculations of qubit correlation functions.Comment: Monitoring a quantum evolution has been adde
Estimating Patterns of Classical and Quantum Skyrmion States
In this review we discuss the latest results concerning development of the
machine learning algorithms for characterization of the magnetic skyrmions that
are topologically-protected magnetic textures originated from the
Dzyaloshinskii-Moriya interaction that competes Heisenberg isotropic exchange
in ferromagnets. We show that for classical spin systems there is a whole pool
of machine approaches allowing their accurate phase classification and
quantitative description on the basis of few magnetization snapshots. In turn,
investigation of the quantum skyrmions is a less explored issue, since there
are fundamental limitations on the simulation of such wave functions with
classical supercomputers. One needs to find the ways to imitate quantum
skyrmions on near-term quantum computers. In this respect, we discuss
implementation of the method for estimating structural complexity of classical
objects for characterization of the quantum skyrmion state on the basis of
limited number of bitstrings obtained from the projective measurements
Glass-based charged particle detector performance for Horizon-T EAS detector system
An implementation of a novel of glass-based detector with fast response and
wide detection range is needed to increase resolution for ultra-high energy
cosmic rays detection. Such detector has been designed and built for the
Horizon-T detector system at Tien Shan high-altitude Science Station. The main
characteristics, such as design, duration of the detector pulse and calibration
of a single particle response are discussed.Comment: Simulation is used to assess glass detector performance. Simulation
is validated first when compared to scintillator detector experimental
measurements. Final results summarized in table. Updated May 2017 with
calibrations updat
An effective spin model on the honeycomb lattice for the description of magnetic properties in two-dimensional FeGeTe
FeGeTe attracts significant attention due to technological
perspectives of realizing room temperature ferromagnetism in two-dimensional
materials. Here we show that due to structural peculiarities of the
FeGeTe monolayer, short distance between the neighboring iron atoms
induces a strong exchange coupling. This strong coupling allows us to consider
them as an effective cluster with a magnetic moment 5 , giving
rise to a simplified spin model on a bipartite honeycomb lattice with the
reduced number of long-range interactions. The simplified model perfectly
reproduces the results of the conventional spin model, but allows for a more
tractable description of the magnetic properties of FeGeTe, which is
important, e.g., for large-scale simulations. Also, we discuss the role of
biaxial strain in the stabilization of ferromagnetic ordering in
FeGeTe.Comment: 7 pages, 7 figure
- …