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Principles of machine learning are applied to models that support skyrmion phases in two dimensions. Successful feature predictions on various phases of the skyrmion model were possible with several layers of convolutional neural network inserted together with several neural network layers. A new training scheme based on features of the input configuration such as magnetization and spin chirality is introduced. It proved possible to further train external parameters such as the magnetic field and temperature and make reliable predictions on them. Algorithms trained on only the z-component or the xy-components of the spin gave equally reliable predictions. The predictive capacity of the algorithm extended to configurations not generated by the original model, but related ones. A procedure for integrating the machine learning algorithm into the interpretation of experimental data is given.
In thin magnetic layers with structural inversion asymmetry and spin-orbit coupling, a Dzyaloshinskii-Moriya interaction arises at the interface. When a spin wave current ${bf j}_m$ flows in a system with a homogeneous magnetization {bf m}, this inte
Machine learning is applied to a large number of modern devices that are essential in building energy efficient smart society. Audio and face recognition are among the most well-known technologies that make use of such artificial intelligence. In mat
We explore remanent magnetization ($mu$) as a function of time and temperature, in a variety of rhombohedral antiferromagnets (AFM) which are also weak ferromagnets (WFM) and piezomagnets (PzM). These measurements, across samples with length scales r
The Dzyaloshinskii-Moriya interaction in ultrathin ferromagnets can result in nonreciprocal propagation of spin waves. We examine theoretically how spin wave power flow is influenced by this interaction. We show that the combination of the dipole-dip
Hamiltonians for general multi-state spin-glass systems with Ising symmetry are derived for both sequential and synchronous updating of the spins. The possibly different behaviour caused by the way of updating is studied in detail for the (anti)-ferr