Density Functional Methods and Applications to Materials Problems

1993 ◽  
Vol 323 ◽  
Author(s):  
Y. S. Li ◽  
M. A. van Daelen ◽  
D. King-Smith ◽  
M. Wrinn ◽  
E. Wimmer ◽  
...  
Keyword(s):  
Density Functional ◽  
Implementation Strategies ◽  
Computer Architectures ◽  
Parallel Computer ◽  
Functional Theory ◽  
Density Functional Methods ◽  
Functional Methods ◽  
Wide Range ◽  
Recent Developments ◽  
And Performance

AbstractDensity functional theory provides a first-principles approach for computing the geometric and electronic structures, and a wealth of corresponding properties, of a wide range of materials types and compositions, including bulk solids, surfaces, defects and clusters of molecules. Parallel advances in hardware performance, implementation strategies and algorithms have all contributed to a rapid growth in the number of important applications. Recent developments under each of these themes are outlined and the breadth of current applications is illustrated by typical examples. Issues associated with the implementation and performance of density functional methods on parallel computer architectures are discussed.

TIME DEPENDENT DENSITY FUNCTIONAL METHODS AND THEIR APPLICATION TO CHEMICAL PHYSICS

2004 ◽  
Vol 03 (01) ◽  
pp. 117-144 ◽  
Author(s):  
AKIRA YOSHIMORI
Keyword(s):  
Density Functional ◽  
Nonlinear Effects ◽  
Functional Method ◽  
Time Dependent ◽  
Solvation Dynamics ◽  
Chemical Physics ◽  
Functional Theory ◽  
Polar Liquids ◽  
Density Functional Methods ◽  
Functional Methods

This article reviews microscopic development of time dependent functional method and its application to chemical physics. It begins with the formulation of density functional theory. The time dependent extension is discussed after the equilibrium formulation. Its application is explained by solvation dynamics. In addition, it reviews studies of nonlinear effects on polar liquids and simple mixtures.

The Electronic Structure and other Properties of Amorphous Silicon Nitride Investigated with Density Functional Theory

2002 ◽  
Vol 715 ◽  
Author(s):  
Peter Kroll
Keyword(s):  
Density Functional ◽  
Elevated Temperatures ◽  
Topological Defects ◽  
Network Models ◽  
Functional Theory ◽  
Chemical Order ◽  
Amorphous Silicon Nitride ◽  
Density Functional Methods ◽  
Functional Methods ◽  
Ab Inito

AbstractStructural models of amorphous silcon nitride, a-Si3N4. consisting of 112-448 atoms were studied using density functional methods. We used continuous random alterating networks with well-defined topology for the respersentation of chemical order in the material as theoretical precursors. The models were optimized within the DFT framework and compared them to one “ab inito derived” model obtained from quenching a hypothetical melt. The strong chemical order is maintained in the network models even after Car-Parrinello molecular dynamic (CPMD) simulations at elevated temperatures for several pico-seconds, In contrast, the “ab initio derived” model exhibits n-N bonds.The optimized strutures of Si3N4 have between 2.6 and 3.2 g/vm3 and comprise few topological defects only. The dominant defects are ever over-coordinated Si and N atoms and the 2-connected is averaged over a dozen modles is, averaged over a dozen models, about 1%. Some models are even free of three-connected Si. The calculated bulk moduli decrease with decreasing density of the a-Si3N4 model. We furthermore investigated the properties of the material ater alloying elements such as H and O, espically their capacity to reduces interal strain.

DENSITY FUNCTIONAL THEORY INVESTIGATE OF THE RgFn(Rg = Kr,Xe; n = 2,4,6) MOLECULES

2011 ◽  
Vol 22 (02) ◽  
pp. 155-167 ◽  
Author(s):  
KAN FANG ◽  
XUEBIN WU ◽  
CHENLEI DU ◽  
YUNCHUAN DAI ◽  
SHIBIN CHU ◽  
...  
Keyword(s):  
Density Functional Theory ◽  
Dft Calculations ◽  
Density Functional ◽  
Theoretical Studies ◽  
Functional Theory ◽  
Dissociation Energies ◽  
Density Functional Methods ◽  
Functional Methods ◽  
Experimental Values ◽  
Hybrid Density Functional

We present a systematic Density Functional Theory (DFT) calculations for the RgFn(Rg = Kr,Xe ; n = 2,4,6) molecules. The dissociation energies, harmonic vibrational frequencies and equilibrium bond lengths of these molecules are determined using several hybrid density functional methods. Results are compared with other theoretical studies and experimental values available. The accuracy of the DFT results is found to depend upon the functionals employed.

scholarly journals Time-dependent density-functional theory in massively parallel computer architectures: the octopus project

2012 ◽  
Vol 24 (23) ◽  
pp. 233202 ◽  
Author(s):  
Xavier Andrade ◽  
Joseba Alberdi-Rodriguez ◽  
David A Strubbe ◽  
Micael J T Oliveira ◽  
Fernando Nogueira ◽  
...  
Keyword(s):  
Density Functional Theory ◽  
Density Functional ◽  
Computer Architectures ◽  
Time Dependent ◽  
Parallel Computer ◽  
Massively Parallel ◽  
Functional Theory ◽  
Dependent Density

Intrinsic Stacking Interactions of Natural and Artificial Nucleobases

2019 ◽  
Author(s):  
Drew P. Harding ◽  
Laura J. Kingsley ◽  
Glen Spraggon ◽  
Steven Wheeler
Keyword(s):  
Gas Phase ◽  
Electrostatic Interactions ◽  
Density Functional ◽  
Molecular Descriptors ◽  
Stacking Interactions ◽  
Interaction Energies ◽  
Functional Theory ◽  
Binding Partner ◽  
Heavy Atoms ◽  
Wide Range

The intrinsic (gas-phase) stacking energies of natural and artificial nucleobases were explored using density functional theory (DFT) and correlated ab initio methods. Ranking the stacking strength of natural nucleobase dimers revealed a preference in binding partner similar to that seen from experiments, namely G > C > A > T > U. Decomposition of these interaction energies using symmetry-adapted perturbation theory (SAPT) showed that these dispersion dominated interactions are modulated by electrostatics. Artificial nucleobases showed a similar stacking preference for natural nucleobases and were also modulated by electrostatic interactions. A robust predictive multivariate model was developed that quantitively predicts the maximum stacking interaction between natural and a wide range of artificial nucleobases using molecular descriptors based on computed electrostatic potentials (ESPs) and the number of heavy atoms. This model should find utility in designing artificial nucleobase analogs that exhibit stacking interactions comparable to those of natural nucleobases. Further analysis of the descriptors in this model unveil the origin of superior stacking abilities of certain nucleobases, including cytosine and guanine.

scholarly journals Solution-Processed OLEDs Based on Thermally Activated Delayed Fluorescence Copper(I) Complexes with Intraligand Charge-Transfer Excited State

Molecules ◽  
2021 ◽  
Vol 26 (4) ◽  
pp. 1125
Author(s):  
Teng Teng ◽  
Jinfan Xiong ◽  
Gang Cheng ◽  
Changjiang Zhou ◽  
Xialei Lv ◽  
...  
Keyword(s):  
Charge Transfer ◽  
Density Functional ◽  
Delayed Fluorescence ◽  
Quantum Yields ◽  
Functional Theory ◽  
Solution Processed ◽  
Thermally Activated Delayed Fluorescence ◽  
Light Emitting ◽  
Thermally Activated ◽  
Wide Range

A new series of tetrahedral heteroleptic copper(I) complexes exhibiting efficient thermally-activated delayed fluorescence (TADF) in green to orange electromagnetic spectral regions has been developed by using D-A type N^N ligand and P^P ligands. Their structures, electrochemical, photophysical, and electroluminescence properties have been characterized. The complexes exhibit high photoluminescence quantum yields (PLQYs) of up to 0.71 at room temperature in doped film and the lifetimes are in a wide range of 4.3–24.1 μs. Density functional theory (DFT) calculations on the complexes reveal the lowest-lying intraligand charge-transfer excited states that are localized on the N^N ligands. Solution-processed organic light emitting diodes (OLEDs) based on one of the new emitters show a maximum external quantum efficiency (EQE) of 7.96%.

scholarly journals A machine learning platform for the discovery of materials

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Carl E. Belle ◽  
Vural Aksakalli ◽  
Salvy P. Russo
Keyword(s):  
Machine Learning ◽  
Density Functional ◽  
Gw Approximation ◽  
Dft Methods ◽  
Functional Theory ◽  
Unit Cell Size ◽  
Learning Platform ◽  
Photovoltaic Materials ◽  
Wide Range ◽  
Quantum Espresso

AbstractFor photovoltaic materials, properties such as band gap $$E_{g}$$ E g are critical indicators of the material’s suitability to perform a desired function. Calculating $$E_{g}$$ E g is often performed using Density Functional Theory (DFT) methods, although more accurate calculation are performed using methods such as the GW approximation. DFT software often used to compute electronic properties includes applications such as VASP, CRYSTAL, CASTEP or Quantum Espresso. Depending on the unit cell size and symmetry of the material, these calculations can be computationally expensive. In this study, we present a new machine learning platform for the accurate prediction of properties such as $$E_{g}$$ E g of a wide range of materials.

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