Theory of ordering in ternary alloys with face-centered-cubic lattices

1968 ◽  
Vol 11 (12) ◽  
pp. 56-63
Author(s):  
V. P. Fadin ◽  
Yu. A. Khon ◽  
V. E. Panin
Keyword(s):  
Ternary Alloys ◽  
Face Centered Cubic ◽  
Cubic Lattices ◽  
Face Centered

Equilibration of kinetic temperatures in face-centered cubic lattices

2020 ◽  
Vol 102 (4) ◽  
Author(s):  
Vitaly A. Kuzkin ◽  
Sergei D. Liazhkov
Keyword(s):  
Face Centered Cubic ◽  
Cubic Lattices ◽  
Face Centered

Zero-Point Effects in Heisenberg Antiferromagnets with Arbitrary Range of Interaction

1972 ◽  
Vol 50 (23) ◽  
pp. 2991-2996 ◽  
Author(s):  
M. F. Collins ◽  
V. K. Tondon
Keyword(s):  
Ground State ◽  
State Energy ◽  
Correction Term ◽  
Zero Point ◽  
Face Centered Cubic ◽  
Heisenberg Antiferromagnet ◽  
Body Centered Cubic ◽  
Cubic Lattices ◽  
Simple Cubic ◽  
Face Centered

The ground state energy, spin-wave energy, and sublattice magnetization have been calculated for a Heisenberg antiferromagnet at the absolute zero of temperature. The treatment extends the earlier work of Anderson, Kubo, and Oguchi to apply for any two-sublattice antiferromagnet with arbitrary range of interaction. It is shown that for each exchange interaction there is a different characteristic correction term to the energies. Explicit calculations are made of these terms for the simple cubic, body-centered cubic, and face-centered cubic lattices, with both first- and second-neighbor interactions. Applications are also made to NiO and MnO. An extra term in the magnetization series beyond that given by earlier workers is derived.

Protein tertiary structure prediction using hidden Markov model based on lattice

2019 ◽  
Vol 17 (02) ◽  
pp. 1950007
Author(s):  
Farzad Peyravi ◽  
Alimohammad Latif ◽  
Seyed Mohammad Moshtaghioun
Keyword(s):  
Amino Acid ◽  
Amino Acid Sequence ◽  
Structure Prediction ◽  
Tertiary Structure ◽  
Hidden Markov ◽  
Face Centered Cubic ◽  
Tertiary Structure Prediction ◽  
Cubic Lattices ◽  
Face Centered ◽  
Protein Tertiary Structure Prediction

The prediction of protein structure from its amino acid sequence is one of the most prominent problems in computational biology. The biological function of a protein depends on its tertiary structure which is determined by its amino acid sequence via the process of protein folding. We propose a novel fold recognition method for protein tertiary structure prediction based on a hidden Markov model and 3D coordinates of amino acid residues. The method introduces states based on the basis vectors in Bravais cubic lattices to learn the path of amino acids of the proteins of each fold. Three hidden Markov models are considered based on simple cubic, body-centered cubic (BCC) and face-centered cubic (FCC) lattices. A 10-fold cross validation was performed on a set of 42 fold SCOP dataset. The proposed composite methodology is compared to fold recognition methods which have HMM as base of their algorithms having approaches on only amino acid sequence or secondary structure. The accuracy of proposed model based on face-centered cubic lattices is quite better in comparison with SAM, 3-HMM optimized and Markov chain optimized in overall experiment. The huge data of 3D space help the model to have greater performance in comparison to methods which use only primary structures or only secondary structures.

scholarly journals Efficient LBM Visual Simulation on Face-Centered Cubic Lattices

2009 ◽  
Vol 15 (5) ◽  
pp. 802-814 ◽  
Author(s):  
K. Petkov ◽  
Feng Qiu ◽  
Zhe Fan ◽  
A.E. Kaufman ◽  
K. Mueller
Keyword(s):  
Visual Simulation ◽  
Face Centered Cubic ◽  
Cubic Lattices ◽  
Face Centered
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