Interpretation of the Infrared Spectra of β-FeSi2 by Molecular Dynamics Simulations

1995 ◽  
Vol 402 ◽  
Author(s):  
Leo Miglio ◽  
Valeria Meregalli
Keyword(s):  
Molecular Dynamics ◽  
Molecular Dynamics Simulations ◽  
Infrared Spectra ◽  
Vibrational Spectra ◽  
Tight Binding ◽  
Binding Potential ◽  
Anisotropic Behaviour ◽  
Dynamics Simulations

AbstractWe report the vibrational spectra of β-FeSi2 calculated by molecular dynamics simulations with a tight binding potential and provide a quantitative confirmation of the anisotropic behaviour found by new Infrared (IR) measurements for small monocrystals This analysis allows for the interpretation of more complex IR results previously found for epitaxial samples.

Finite Temperature Infrared Spectra from Polarizable Molecular Dynamics Simulations

2014 ◽  
Vol 10 (8) ◽  
pp. 3190-3199 ◽  
Author(s):  
David Semrouni ◽  
Ashwani Sharma ◽  
Jean-Pierre Dognon ◽  
Gilles Ohanessian ◽  
Carine Clavaguéra
Keyword(s):  
Molecular Dynamics ◽  
Molecular Dynamics Simulations ◽  
Infrared Spectra ◽  
Finite Temperature ◽  
Dynamics Simulations

scholarly journals Tight-Binding Molecular Dynamics Simulations on Point Defects Diffusion and Interactions in Crystalline Silicon

1995 ◽  
Vol 396 ◽  
Author(s):  
M. tang ◽  
L. colombo ◽  
T. Diaz De La Rubia
Keyword(s):  
Molecular Dynamics ◽  
Molecular Dynamics Simulations ◽  
Point Defects ◽  
Crystalline Silicon ◽  
Tight Binding ◽  
Diffusion Path ◽  
Binding Energies ◽  
Defect Clusters ◽  
Dynamics Simulations

AbstractTight-binding molecular dynamics (TBMD) simulations are performed (i) to evaluate the formation and binding energies of point defects and defect clusters, (ii) to compute the diffusivity of self-interstitial and vacancy in crystalline silicon, and (iii) to characterize the diffusion path and mechanism at the atomistic level. In addition, the interaction between individual defects and their clustering is investigated.

scholarly journals Multiscale QM/MM Molecular Dynamics Simulations of the Trimeric Major Light-Harvesting Complex II

2021 ◽  
Author(s):  
Sayan Maity ◽  
Vangelis Daskalakis ◽  
Marcus Elstner ◽  
Ulrich Kleinekathöfer
Keyword(s):  
Molecular Dynamics ◽  
Molecular Dynamics Simulations ◽  
Chlorophyll A ◽  
Density Functional ◽  
Light Harvesting ◽  
Higher Plants ◽  
Tight Binding ◽  
Spectral Densities ◽  
Light Harvesting Complex ◽  
Dynamics Simulations

Photosynthetic processes are driven by sunlight. Too little of it and the photosynthetic machinery cannot produce the reductive power to drive the anabolic pathways. Too much sunlight and the machinery can get damaged. In higher plants, the major Light Harvesting Complex (LHCII) efficiently absorbs the light energy, but can also dissipate it when in excess (quenching). In order to study the dynamics related to the quenching process but also the exciton dynamics in general, one needs to accurately determine the so-called spectral density which describes the coupling between the relevant pigment modes and the environmental degrees of freedom. To this end, Born–Oppenheimer molecular dynamics simulations in a quantum mechanics/molecular mechanics (QM/MM) fashion utilizing the density functional based tight binding (DFTB) method have been performed for the ground state dynamics. Subsequently, the time-dependent extension of the long-range-corrected DFTB scheme has been employed for the excited state calculations of the individual chlorophyll-a molecules in the LHCII complex. The analysis of this data resulted in spectral densities showing an astonishing agreement with the experimental counterpart in this rather large system. This consistency with an experimental observable also supports the accuracy, robustness, and reliability of the present multi-scale scheme. In addition, the resulting spectral densities and site energies were used to determine the exciton transfer rate within a special pigment pair consisting of a chlorophyll-a and a carotenoid molecule which is assumed to play a role in the balance between the light harvesting and quenching modes.

An Ab-Initio Multicenter Tight-Binding Model for Molecular Dynamics Simulations

1988 ◽  
Vol 141 ◽  
Author(s):  
Otto F. Sankey ◽  
David J. Niklewski
Keyword(s):  
Molecular Dynamics ◽  
Molecular Dynamics Simulations ◽  
Local Density ◽  
Tight Binding ◽  
Real Space ◽  
Hamiltonian Matrix ◽  
Binding Model ◽  
Annealing Study ◽  
Total Energies ◽  
Dynamics Simulations

AbstractA new, approximate method has been developed for computing total energies and forces for a variety of applications including molecular dynamics simulations of covalent materials. The method is tight-binding-like and is based on the local density approximation within the pseudopotential scheme. Slightly excited pseudo-atomic-orbitals are used, and the tight-binding Hamiltonian matrix is obtained in real space. The method is used to find the total energies for five crystalline phases of Si and the Si 2 molecule. Excellent agreement is found with experiment. A molecular dynamics simulated annealing study has been performed on the Si 3 molecule to determine the ground state configuration.

Tight-Binding Molecular Dynamics Study of Liquid and Amorphous Carbon

1992 ◽  
Vol 291 ◽  
Author(s):  
C. Z. Wang ◽  
K. M. Ho ◽  
C. T. Chan
Keyword(s):  
Experimental Data ◽  
Molecular Dynamics ◽  
Ab Initio ◽  
Molecular Dynamics Simulations ◽  
Amorphous Carbon ◽  
Simulation Study ◽  
Tight Binding ◽  
Sufficient Accuracy ◽  
Complex Carbon ◽  
Dynamics Simulations

ABSTRACTTight-binding molecular-dynamics simulations are performed to study the structure of liquid and amorphous carbon. Comparisons of our results with ab initiomolecular dynamics (Car-Parrinello) results and experimental data show that the scheme has sufficient accuracy and efficiency for realistic simulation study of the structural properties of complex carbon systems.

scholarly journals Artificial neural network correction for density-functional tight-binding molecular dynamics simulations

2019 ◽  
Vol 9 (3) ◽  
pp. 867-873 ◽  
Author(s):  
Junmian Zhu ◽  
Van Quan Vuong ◽  
Bobby G. Sumpter ◽  
Stephan Irle
Keyword(s):  
Neural Network ◽  
Molecular Dynamics ◽  
Artificial Neural Network ◽  
Molecular Dynamics Simulations ◽  
Density Functional ◽  
Tight Binding ◽  
Artificial Neural ◽  
Dynamics Simulations ◽  
Image Position

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