scholarly journals Vibrational Study of Iodide-Based Room-Temperature Ionic-Liquid Effects on Candidate N719-Chromophore/Titania Interfaces for Dye-Sensitised Solar-Cell Applications from Ab-Initio Based Molecular-Dynamics Simulation

Energies ◽  
2018 ◽  
Vol 11 (10) ◽  
pp. 2570 ◽  
Author(s):  
Yogeshwaran Krishnan ◽  
Aaron Byrne ◽  
Niall English
Keyword(s):  
Molecular Dynamics ◽  
Ab Initio ◽  
Vibrational Spectra ◽  
Density Functional ◽  
Room Temperature ◽  
Dynamics Simulation ◽  
Ab Initio Molecular Dynamics ◽  
Experimental Frequency ◽  
Velocity Autocorrelation ◽  
Anatase Titania

The accurate ab-initio modelling of prototypical and well-representative photo-active interfaces for candidate dye-sensitised solar cells is a challenging problem. To this end, using ab-initio molecular-dynamics (AIMD) simulation based on Density Functional Theory (DFT), the effects of explicit solvation by iodide-based, I−[bmim]+ room-temperature ionic liquids (RTILs) have been assessed on modelling a N719-chromophore sensitising dye adsorbed onto an anatase-titania (101) surface. In particular, the vibrational spectra for this model photo-active interface were calculated by means of Fourier transformed mass-weighted velocity autocorrelation functions. These were compared with experiment and against each other to gain an understanding of how using iodine-based RTILs as the electrolytic hole acceptor alters the dynamical properties of the widely-used N719 dye. The effect of Perdew-Burke-Ernzerhof (PBE) and Becke-Lee-Yang-Parr (BLYP) functionals on the vibrational spectra were assessed. PBE generally performed best in producing spectra which matched the typically expected experimental frequency modes.

Structural stabilities and transformations in cationized asparagine at finite temperatures: An ab initio molecular dynamics study

2014 ◽  
Vol 13 (04) ◽  
pp. 1450024
Author(s):  
Shoutian Sun ◽  
Jianwen Liu ◽  
Zhi-Feng Liu
Keyword(s):  
Molecular Dynamics ◽  
Ab Initio ◽  
Structural Transformation ◽  
Vibrational Spectra ◽  
Room Temperature ◽  
Ab Initio Molecular Dynamics ◽  
And Shifts ◽  
Torsional Angles

The cationic complexes of Asparagine (Asn), M +( Asn ), with M + = Li +, Na +, K +, Cs +, and H +, are models for studying the interaction between cations and Asn. Ab initio molecular dynamics (AIMD) method is employed to simulate their behavior at finite temperatures. Structural transformation between conformers is observed, which becomes progressively easier as the cation varies from Li +, to Na +, K +, Cs +, and H +. The fluctuation of the M +– N and M +– O distances and rotation of torsional angles are significant even at room temperature for K +, Cs + and H +. Vibrational profiles based on AIMD trajectories provide insights into the broadening and shifts in relative intensities observed in the vibrational spectra measured by infrared multi-photon dissociation (IRMPD) experiments.

Energetics and diffusion of liquid water and hydrated ions through nanopores in graphene: ab initio molecular dynamics simulation

2017 ◽  
Vol 19 (31) ◽  
pp. 20551-20558 ◽  
Author(s):  
Raúl Guerrero-Avilés ◽  
Walter Orellana
Keyword(s):  
Molecular Dynamics ◽  
Ab Initio ◽  
Density Functional ◽  
Density Functional Theory Calculations ◽  
Md Simulations ◽  
Dynamics Simulation ◽  
Ab Initio Molecular Dynamics ◽  
Functional Theory ◽  
Hydrated Ions ◽  
And Diffusion

The energetics and diffusion of water molecules and hydrated ions (Na+, Cl−) passing through nanopores in graphene are addressed by dispersion-corrected density functional theory calculations and ab initio molecular dynamics (MD) simulations.

Ab Initio Molecular Dynamics Simulation of Zinc metalloproteins with Enhanced Self-Organizing Incremental High Dimensional Neural Network

2020 ◽  
Author(s):  
Mingyuan Xu ◽  
Tong Zhu ◽  
John ZH Zhang
Keyword(s):  
Neural Network ◽  
Molecular Dynamics ◽  
Ab Initio ◽  
Density Functional ◽  
Structure Refinement ◽  
Dynamics Simulation ◽  
Ab Initio Molecular Dynamics ◽  
Neural Network Approach ◽  
Quantum Chemistry Calculations ◽  
Molecular Potentials

<p>Artificial neural network provides the possibility to develop molecular potentials with both the efficiency of the classical molecular mechanics and the accuracy of the quantum chemical methods. In this work, we developed ab initio based neural network potential (NN/MM-RESP-MBG) to perform molecular dynamics study for metalloproteins. The interaction energy, atomic forces, and atomic charges of metal binding group in NN/MM-RESP-MBG are described by a neural network potential trained with energies and forces generated from density functional calculations. Here, we used our recently proposed E-SOI-HDNN model to achieve the automatic construction of reference dataset of metalloproteins and the active learning of neural network potential functions. The predicted energies and atomic forces from the NN potential show excellent agreement with the quantum chemistry calculations. Using this approach, we can perform long time AIMD simulations and structure refinement MD simulation for metalloproteins. In 1 ns AIMD simulation of four common coordination mode of zinc-containing metalloproteins, the statistical average structure is in good agreement with statistic value of PDB Bank database. The neural network approach used in this study can be applied to construct potentials to metalloproteinase catalysis, ligand binding and other important biochemical processes and its salient features can shed light on the development of more accurate molecular potentials for metal ions in other biomacromolecule system. </p>

Ab Initio Molecular Dynamics Simulation of Zinc metalloproteins with Enhanced Self-Organizing Incremental High Dimensional Neural Network

2020 ◽  
Author(s):  
Mingyuan Xu ◽  
Tong Zhu ◽  
John ZH Zhang
Keyword(s):  
Neural Network ◽  
Molecular Dynamics ◽  
Ab Initio ◽  
Density Functional ◽  
Structure Refinement ◽  
Dynamics Simulation ◽  
Ab Initio Molecular Dynamics ◽  
Neural Network Approach ◽  
Quantum Chemistry Calculations ◽  
Molecular Potentials

<p>Artificial neural network provides the possibility to develop molecular potentials with both the efficiency of the classical molecular mechanics and the accuracy of the quantum chemical methods. In this work, we developed ab initio based neural network potential (NN/MM-RESP-MBG) to perform molecular dynamics study for metalloproteins. The interaction energy, atomic forces, and atomic charges of metal binding group in NN/MM-RESP-MBG are described by a neural network potential trained with energies and forces generated from density functional calculations. Here, we used our recently proposed E-SOI-HDNN model to achieve the automatic construction of reference dataset of metalloproteins and the active learning of neural network potential functions. The predicted energies and atomic forces from the NN potential show excellent agreement with the quantum chemistry calculations. Using this approach, we can perform long time AIMD simulations and structure refinement MD simulation for metalloproteins. In 1 ns AIMD simulation of four common coordination mode of zinc-containing metalloproteins, the statistical average structure is in good agreement with statistic value of PDB Bank database. The neural network approach used in this study can be applied to construct potentials to metalloproteinase catalysis, ligand binding and other important biochemical processes and its salient features can shed light on the development of more accurate molecular potentials for metal ions in other biomacromolecule system. </p>

An Ab Initio Molecular Dynamics Simulation of Femtosecond Laser Processing of Germanium

2013 ◽  
Author(s):  
Pengfei Ji ◽  
Yuwen Zhang
Keyword(s):  
Molecular Dynamics ◽  
Femtosecond Laser ◽  
Ab Initio ◽  
Laser Processing ◽  
Density Functional ◽  
Dynamic Properties ◽  
Dynamics Simulation ◽  
Femtosecond Laser Irradiation ◽  
Ab Initio Molecular Dynamics ◽  
Femtosecond Laser Processing

An ab initio molecular dynamics study of femtosecond laser processing of germanium is presented in this paper. The method based on the finite temperature density functional theory is adopted to probe the nanostructure change, thermal motion of the atoms, dynamic property of the velocity autocorrelation, and the vibrational density of states. Starting from a cubic system at room temperature (300 K) containing 64 germanium atoms with an ordered arrangement of 1.132 nm in each dimension, the femtosecond laser processing is simulated by imposing the Nose Hoover thermostat to the electron subsystem lasting for ∼100 fs and continuing with microcanonical ensemble simulation of ∼200 fs. The simulation results show solid, liquid and gas phases of germanium under adjusted intensities of the femtosecond laser irradiation. We find the irradiated germanium distinguishes from the usual germanium crystal by analyzing their melting and dynamic properties.

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