On the classification of equilibrium superfluid states with scalar and tensor order parameters

2002 ◽  
Vol 28 (4) ◽  
pp. 227-234 ◽  
Author(s):  
M. Yu. Kovalevsky ◽  
S. V. Peletminsky ◽  
N. N. Chekanova
Keyword(s):  
Order Parameters ◽  
Tensor Order

scholarly journals Neural-network-based order parameters for classification of binary hard-sphere crystal structures

2018 ◽  
Vol 116 (21-22) ◽  
pp. 3066-3075 ◽  
Author(s):  
Emanuele Boattini ◽  
Michel Ram ◽  
Frank Smallenburg ◽  
Laura Filion
Keyword(s):  
Neural Network ◽  
Crystal Structures ◽  
Hard Sphere ◽  
Order Parameters

Classification of the equilibrium states of magnets with vector and quadrupole order parameters

2007 ◽  
Vol 33 (11) ◽  
pp. 965-973 ◽  
Author(s):  
D. A. Dem’yanenko ◽  
M. Yu. Kovalevskiĭ
Keyword(s):  
Order Parameters ◽  
Equilibrium States

Classification of continuous phase transitions and stable phases. I. Six-dimensional order parameters

1986 ◽  
Vol 33 (3) ◽  
pp. 1774-1788 ◽  
Author(s):  
Jai Sam Kim ◽  
Dorian M. Hatch ◽  
Harold T. Stokes
Keyword(s):  
Phase Transitions ◽  
Continuous Phase ◽  
Order Parameters

scholarly journals Equilibrium order parameters of nematic liquid crystals in the Landau-de Gennes theory

2010 ◽  
Vol 21 (2) ◽  
pp. 181-203 ◽  
Author(s):  
APALA MAJUMDAR
Keyword(s):  
Three Dimensional ◽  
Energy Functional ◽  
Order Parameters ◽  
Tensor Order ◽  
Temperature Regimes ◽  
Explicit Bounds ◽  
Scalar Order ◽  
Definition Of ◽  
Gennes Theory ◽  
The Continuum

We study equilibrium liquid crystal configurations in three-dimensional geometries, within the continuum Landau-de Gennes theory. We obtain explicit bounds for the equilibrium scalar order parameters in terms of the temperature and material-dependent constants. We explicitly quantify the temperature regimes where the Landau-de Gennes predictions match and the temperature regimes where the Landau-de Gennes predictions do not match the probabilistic second-moment definition of the Q-tensor order parameter. The regime of agreement may be interpreted as the regime of validity of the Landau-de Gennes theory since the Landau-de Gennes theory predicts large values of the equilibrium scalar order parameters – larger than unity, in the low-temperature regime. We discuss a modified Landau-de Gennes energy functional which yields physically realistic values of the equilibrium scalar order parameters in all temperature regimes.

Classification of continuous phase transitions and stable phases. II. Four-dimensional order parameters

1986 ◽  
Vol 33 (9) ◽  
pp. 6210-6230 ◽  
Author(s):  
Jai Sam Kim ◽  
Harold T. Stokes ◽  
Dorian M. Hatch
Keyword(s):  
Phase Transitions ◽  
Continuous Phase ◽  
Order Parameters

scholarly journals Негейзенберговский ферримагентик с одноионной анизотропией

2022 ◽  
Vol 64 (3) ◽  
pp. 337
Author(s):  
О.А. Космачев ◽  
Е.А. Ярыгина ◽  
Я.Ю. Матюнина ◽  
Ю.А. Фридман
Keyword(s):  
Phase Transition ◽  
Phase Diagram ◽  
Exchange Interaction ◽  
Order Parameters ◽  
Easy Plane ◽  
Heisenberg Exchange ◽  
Ferrimagnetic Phase ◽  
Tensor Order ◽  
Plane Type

We have investigated the effect of single-ion anisotropy of the "easy plane" type on the phase states of a ferrimagnet with S = 1 and σ=1/2 sublattices and non-Heisenberg (bilinear and biquadratic in spins) exchange interaction for the sublattice with S = 1. It is shown that taking into account both the non-Heisenberg exchange interaction and the single-ion anisotropy of the sublattice with S = 1 leads to the realization of a phase with vector order parameters (ferrimagnetic phase) and a phase characterized by both vector and tensor order parameters (quadrupole-ferrimagnetic). It is shown that taking into account single-ion anisotropy changes the type of phase transition in comparison with an isotropic non-Heisenberg ferrimagnet. A phase diagram is constructed, and the condition for the compensation of the sublattice spins is determined.

scholarly journals GCIceNet: a graph convolutional network for accurate classification of water phases

2020 ◽  
Vol 22 (45) ◽  
pp. 26340-26350
Author(s):  
QHwan Kim ◽  
Joon-Hyuk Ko ◽  
Sunghoon Kim ◽  
Wonho Jhe
Keyword(s):  
Unsupervised Learning ◽  
Order Parameters ◽  
Water Molecules ◽  
Convolutional Network ◽  
Convolutional Networks ◽  
Supervised And Unsupervised Learning

We develop GCIceNet, which automatically generates machine-based order parameters for classifying the phases of water molecules via supervised and unsupervised learning with graph convolutional networks.

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