Moscow Institute of Physics and Technology

About: Moscow Institute of Physics and Technology is a education organization based out in Dolgoprudnyy, Russia. It is known for research contribution in the topics: Laser & Plasma. The organization has 8594 authors who have published 16968 publications receiving 246551 citations. The organization is also known as: MIPT & Moscow Institute of Physics and Technology (State University).

The reversal and chaotic attractor in the nonholonomic model of Chaplygin’s top

Abstract: In this paper we consider the motion of a dynamically asymmetric unbalanced ball on a plane in a gravitational field. The point of contact of the ball with the plane is subject to a nonholonomic constraint which forbids slipping. The motion of the ball is governed by the nonholonomic reversible system of 6 differential equations. In the case of arbitrary displacement of the center of mass of the ball the system under consideration is a nonintegrable system without an invariant measure. Using qualitative and quantitative analysis we show that the unbalanced ball exhibits reversal (the phenomenon of reversal of the direction of rotation) for some parameter values. Moreover, by constructing charts of Lyaponov exponents we find a few types of strange attractors in the system, including the so-called figure-eight attractor which belongs to the genuine strange attractors of pseudohyperbolic type.

IR differential-absorption lidars for ecological monitoring of the environment

Abstract: A review of studies on lidar sensing of the environment by the method of IR differential absorption is presented The differential-absorption method is described and its various applications are considered A comparison of this method with other methods of lidar sensing showed that a differential-absorption lidar successfully supplements a Raman lidar The basic parameters are presented for IR lidars fabricated recently by various research groups The outlook for the IR lidar sensing of the atmosphere is discussed

Ferromagnet/Superconductor Hybridization for Magnonic Applications

Abstract: In this work, a new hybridization of superconducting and ferromagnetic orders is demonstrated, promising for magnonics. By measuring the ferromagnetic and spin wave resonance absorption spectra of a magnetostatically coupled permalloy/niobium bilayer at different temperatures, magnetostatic spin wave resonances with unconventional dispersion are observed. The mechanism behind the modified dispersion, confirmed with micromagnetic simulations, implies screening of the alternating magnetostatic stray fields of precessing magnetic moments in the ferromagnetic layer by the superconducting surface in the Meissner state.

Odd-frequency superconducting states with different types of Meissner response: Problem of coexistence

Abstract: We consider physical properties of a superconductor with a recently proposed type of odd-frequency pairing that exhibits diamagnetic Meissner response (``odd-dia state''). Such a state was suggested in order to address stability issues arising in an odd-frequency superconducting state with paramagnetic Meissner response (``odd-para state''). Assuming the existence of an odd-dia state (due to a proper retarded interaction), we study its coexistence with an odd-para state. The latter is known to be generated as an induced superconducting component in, e.g., singlet superconductor/ferromagnet proximity structures or triplet superconductor/normal metal systems. Calculating the superfluid density of the mixed odd-para/odd-dia state and the Josephson current between the odd-para and odd-dia states, we find that the expressions for the currents in both cases have nonvanishing imaginary contributions and are therefore unphysical. We show that a realization of the odd-dia state implies the absence of a Hamiltonian description of the system, and suggest that there exists no physically realizable perturbation that could give rise to the spontaneous symmetry breaking necessary for an actual realization of the odd-dia superconducting state.

Energy-free machine learning force field for aluminum

Abstract: We used the machine learning technique of Li et al. (PRL 114, 2015) for molecular dynamics simulations. Atomic configurations were described by feature matrix based on internal vectors, and linear regression was used as a learning technique. We implemented this approach in the LAMMPS code. The method was applied to crystalline and liquid aluminum and uranium at different temperatures and densities, and showed the highest accuracy among different published potentials. Phonon density of states, entropy and melting temperature of aluminum were calculated using this machine learning potential. The results are in excellent agreement with experimental data and results of full ab initio calculations.