Density Functional Theory (DFT) Overview.pptxmomnaqayyum01
油
Density Functional Theory (DFT) is a powerful computational method used to study the electronic structure of molecules and materials by focusing on electron density rather than the many-body wave function. DFT is preferred due to its efficiency, accuracy, and versatility, making it applicable in diverse fields like material design, catalysis, and drug discovery. When applied to the HO molecule, DFT accurately predicts its molecular geometry, bond angles, and bond lengths, and provides insights into its electron density distribution, which reveals its polar nature. DFT simplifies the study of many-particle systems by reducing the problem to a manageable form, allowing for efficient calculations of large systems. The Born-Oppenheimer approximation further simplifies DFT by treating nuclear and electronic motions separately, significantly reducing the computational cost. At its core, the Hohenberg-Kohn theorem provides the theoretical foundation of DFT, stating that all properties of a quantum system can be determined by its electron density, making it a cornerstone of modern computational chemistry and materials science. In this PPT, we have explained the fundamentals of Density Functional Theory (DFT), its importance, and its application to the HO molecule, along with key concepts like electron density, many-particle systems, the Born-Oppenheimer approximation, and the Hohenberg-Kohn theorem, highlighting its relevance in modern computational chemistry.
This document provides an overview of density functional theory (DFT). It discusses the history and development of DFT, including the Hohenberg-Kohn and Kohn-Sham theorems. The document outlines the fundamentals of DFT, including how it uses functionals of electron density rather than wavefunctions to simplify solving the many-body Schrodinger equation. It also describes the self-consistent approach in DFT calculations and provides examples of popular DFT software packages.
Computational chemistry uses computers to simulate chemical systems and solve equations that model their properties. It is considered a third pillar of scientific investigation, along with theory and experiment. There are several computational methodologies including quantum mechanics, molecular mechanics, and molecular dynamics. Computational chemistry software can be used to optimize molecular geometries, map potential energy surfaces, perform conformational analyses, and calculate many other molecular properties and reaction kinetics. These methods have improved significantly with increasing computer power over the past few decades.
This document discusses thermoelectric materials and calculations using the Wien2K software. It describes the Seebeck effect and Peltier effect. It discusses using Wien2K to model materials like Mg2Si, calculate properties like density of states, band structure, and optimize volume. Modifying approximations, strain effects, and nanostructuring are discussed to increase thermoelectric figure of merit ZT by increasing power factor and decreasing thermal conductivity.
SCF methods, basis sets, and integrals part IIIAkefAfaneh2
油
Some DFT implementations (such as Octopus) attempt to describe the molecular
KohnSham orbitals on a real-space grid.
A 3D simulation box is chosen together with a grid spacing, for example 0.5 a0. Then,
a grid in 3D is constructed and the SCF equations are solved on the grid.
This is different from an MO-LCAO expansion in numerical AOs!
Pseudopotentials are inevitable for real-space grid methods, but they are not required
when numerical AOs are used.
A great advantage of the use of numerical AOs as in DMol3 is that the method is free
of the basis-set superposition error (BSSE).
Because exact atomic orbitals are used, the atoms in a molecule cannot improve
their orbitals arti鍖cially using basis functions from other atoms.
Computational chemistry uses mathematical and computing methods to simulate chemical processes. It can predict molecular properties, structures, interactions and reaction pathways without expensive experiments. The main computational methods are ab initio, semi-empirical, density functional theory, molecular mechanics and molecular dynamics. Geometry optimization finds the lowest energy conformation of a molecule using algorithms to minimize the potential energy surface. It is important for understanding how structure influences properties and reactivity.
The document discusses ab initio molecular dynamics simulation methods. It begins by introducing molecular dynamics and Monte Carlo simulations using empirical potentials. It then describes limitations of empirical potentials and the need for ab initio molecular dynamics which calculates the potential from quantum mechanics. The document outlines several ab initio molecular dynamics methods including Ehrenfest molecular dynamics, Born-Oppenheimer molecular dynamics, and Car-Parrinello molecular dynamics. It provides details on how these methods treat the quantum mechanical potential and classical nuclear motion.
Computational Chemistry aspects of Molecular Mechanics and Dynamics have been discussed in this presentation. Useful for the Undergraduate and Postgraduate students of Pharmacy, Drug Design and Computational Chemistry
This document discusses challenges and open questions in nuclear density functional theory (DFT). It begins by providing background on DFT and how it has been applied to nuclei using approximations like the local density approximation. It then discusses questions around improving the nuclear energy density functional, including justifying terms from microscopic theory, improving treatment of pairing and beyond-mean-field correlations, and incorporating dynamics. The document concludes by emphasizing the need for focused theoretical efforts, international collaborations, and new experimental data to help address open questions in nuclear DFT.
Advantages and applications of computational chemistrymanikanthaTumarada
油
The document discusses computational chemistry methods for calculating various thermodynamic and electronic properties of molecules. It provides an overview of computational chemistry and the properties that can be calculated, such as structure, energy, dipole moment, polarizability, ionization potential, HOMO/LUMO energies, chemical hardness and softness. It also describes different computational methods like classical molecular mechanics and molecular dynamics, as well as quantum chemistry methods including semi-empirical, ab initio and density functional theory approaches. Specific examples are given of calculating properties like dipole moment, polarizability, ionization potential using computational methods.
Computational chemistry uses theoretical chemistry calculations incorporated into computer programs to calculate molecular structures and properties. It can calculate properties such as structure, energy, charge distribution, and spectroscopic quantities using methods that range from highly accurate ab initio methods to less accurate semi-empirical and molecular mechanics methods. Computational chemistry allows medicinal chemists to use computer power to measure molecular geometry, electron density, energies, and more for applications such as conformational analysis, docking ligands in receptor sites, and comparing ligands.
Density functional theory (DFT) is a computational quantum mechanics method used to investigate the electronic structure of many-body systems like molecules and solids. It functions by using functionals of the electron density rather than the many-body wavefunction. This makes calculations more efficient. DFT was developed based on the Hohenberg-Kohn theorems, which established that all ground state properties are uniquely determined by the electron density alone. This allowed modeling systems using functionals of the density rather than attempting to solve the complicated many-electron Schrodinger equation directly. DFT is now widely used in physics, chemistry, and materials science.
Single Particle Appoximation Final Pres.pptxkhalilpcsir
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Many Body Problems topic About Perturbation Theory Single Particle Aproximation, it Defination Working , advantgaes Limittation Future aspects , Mathemtical Formulas about Many Body Problems topic About Perturbation Theory Single Particle Aproximation, Detail of Similicity in Quantam phyisics Methods
The document discusses using machine learning to develop density functional approximations for orbital-free density functional theory calculations. Specifically, kernel ridge regression is used to approximate the kinetic energy of non-interacting fermions confined to a 1D box as a functional of electron density. This machine-learned density functional approximation achieves highly accurate energies and self-consistent densities, outperforming traditional approximations. Various kernels, cross-validation methods, and representations of the electron density are explored to optimize the machine-learned approximation.
Computational chemistry uses numerical simulations based on the laws of physics to model chemical structures and reactions. There are different types of computational models of varying accuracy and computational cost, including molecular mechanics, semi-empirical, ab initio, and density functional theory methods. The accuracy of calculations also depends on the basis set used to describe molecular orbitals. Computational chemistry has become an important tool for characterizing nanomaterials.
This document provides an introduction to computational quantum chemistry. It defines computational chemistry as using mathematical approximations and computer programs to solve chemical problems based on quantum mechanics. Specifically, computational quantum chemistry focuses on solving the Schr旦dinger equation for molecular systems using approximations like the Born-Oppenheimer approximation. It discusses how computational methods can be used to calculate various molecular properties and motivates the need for approximations due to the inability to exactly solve the Schr旦dinger equation for complex molecules. The document then provides an overview of common computational methods like Hartree-Fock, configuration interaction, M淡ller-Plesset perturbation theory, and coupled cluster theory.
This document provides an introduction to computational quantum chemistry. It defines computational chemistry as using mathematical approximations and computer programs to solve chemical problems based on quantum mechanics. Specifically, computational quantum chemistry focuses on solving the Schr旦dinger equation for molecular systems using approximations like the Born-Oppenheimer approximation. It also discusses methods for approximating the wavefunction like Hartree-Fock, configuration interaction, and density functional theory as well as expanding the molecular orbitals in a basis set of atomic orbitals.
The document summarizes the hopping mechanism for charge transport in organic materials. It describes how charge transport occurs through hopping between molecules, with the hopping rate determined by Marcus electron transfer theory. It discusses methods to calculate key parameters in the Marcus rate formula, including the transfer integral between molecules and the reorganization energy, using first-principles density functional theory calculations. These parameters and the Marcus rate theory can then be used to simulate charge mobility through random walk models.
Molecular and Quantum Mechanics in drug designAjay Kumar
油
This document discusses and compares molecular mechanics and quantum mechanics methods for drug design. It provides an overview of molecular mechanics, which uses classical physics to model potential energy surfaces, and common molecular mechanics force fields such as AMBER and CHARMM. It also describes quantum mechanics principles, density functional theory, and semi-empirical methods. Key differences between molecular mechanics and quantum mechanics are noted, such as system size, time required, and accuracy. Applications of each method in drug design are mentioned.
This document discusses thermoelectric materials and calculations using the Wien2K software. It describes the Seebeck effect and Peltier effect. It discusses using Wien2K to model materials like Mg2Si, calculate properties like density of states, band structure, and optimize volume. Modifying approximations, strain effects, and nanostructuring are discussed to increase thermoelectric figure of merit ZT by increasing power factor and decreasing thermal conductivity.
SCF methods, basis sets, and integrals part IIIAkefAfaneh2
油
Some DFT implementations (such as Octopus) attempt to describe the molecular
KohnSham orbitals on a real-space grid.
A 3D simulation box is chosen together with a grid spacing, for example 0.5 a0. Then,
a grid in 3D is constructed and the SCF equations are solved on the grid.
This is different from an MO-LCAO expansion in numerical AOs!
Pseudopotentials are inevitable for real-space grid methods, but they are not required
when numerical AOs are used.
A great advantage of the use of numerical AOs as in DMol3 is that the method is free
of the basis-set superposition error (BSSE).
Because exact atomic orbitals are used, the atoms in a molecule cannot improve
their orbitals arti鍖cially using basis functions from other atoms.
Computational chemistry uses mathematical and computing methods to simulate chemical processes. It can predict molecular properties, structures, interactions and reaction pathways without expensive experiments. The main computational methods are ab initio, semi-empirical, density functional theory, molecular mechanics and molecular dynamics. Geometry optimization finds the lowest energy conformation of a molecule using algorithms to minimize the potential energy surface. It is important for understanding how structure influences properties and reactivity.
The document discusses ab initio molecular dynamics simulation methods. It begins by introducing molecular dynamics and Monte Carlo simulations using empirical potentials. It then describes limitations of empirical potentials and the need for ab initio molecular dynamics which calculates the potential from quantum mechanics. The document outlines several ab initio molecular dynamics methods including Ehrenfest molecular dynamics, Born-Oppenheimer molecular dynamics, and Car-Parrinello molecular dynamics. It provides details on how these methods treat the quantum mechanical potential and classical nuclear motion.
Computational Chemistry aspects of Molecular Mechanics and Dynamics have been discussed in this presentation. Useful for the Undergraduate and Postgraduate students of Pharmacy, Drug Design and Computational Chemistry
This document discusses challenges and open questions in nuclear density functional theory (DFT). It begins by providing background on DFT and how it has been applied to nuclei using approximations like the local density approximation. It then discusses questions around improving the nuclear energy density functional, including justifying terms from microscopic theory, improving treatment of pairing and beyond-mean-field correlations, and incorporating dynamics. The document concludes by emphasizing the need for focused theoretical efforts, international collaborations, and new experimental data to help address open questions in nuclear DFT.
Advantages and applications of computational chemistrymanikanthaTumarada
油
The document discusses computational chemistry methods for calculating various thermodynamic and electronic properties of molecules. It provides an overview of computational chemistry and the properties that can be calculated, such as structure, energy, dipole moment, polarizability, ionization potential, HOMO/LUMO energies, chemical hardness and softness. It also describes different computational methods like classical molecular mechanics and molecular dynamics, as well as quantum chemistry methods including semi-empirical, ab initio and density functional theory approaches. Specific examples are given of calculating properties like dipole moment, polarizability, ionization potential using computational methods.
Computational chemistry uses theoretical chemistry calculations incorporated into computer programs to calculate molecular structures and properties. It can calculate properties such as structure, energy, charge distribution, and spectroscopic quantities using methods that range from highly accurate ab initio methods to less accurate semi-empirical and molecular mechanics methods. Computational chemistry allows medicinal chemists to use computer power to measure molecular geometry, electron density, energies, and more for applications such as conformational analysis, docking ligands in receptor sites, and comparing ligands.
Density functional theory (DFT) is a computational quantum mechanics method used to investigate the electronic structure of many-body systems like molecules and solids. It functions by using functionals of the electron density rather than the many-body wavefunction. This makes calculations more efficient. DFT was developed based on the Hohenberg-Kohn theorems, which established that all ground state properties are uniquely determined by the electron density alone. This allowed modeling systems using functionals of the density rather than attempting to solve the complicated many-electron Schrodinger equation directly. DFT is now widely used in physics, chemistry, and materials science.
Single Particle Appoximation Final Pres.pptxkhalilpcsir
油
Many Body Problems topic About Perturbation Theory Single Particle Aproximation, it Defination Working , advantgaes Limittation Future aspects , Mathemtical Formulas about Many Body Problems topic About Perturbation Theory Single Particle Aproximation, Detail of Similicity in Quantam phyisics Methods
The document discusses using machine learning to develop density functional approximations for orbital-free density functional theory calculations. Specifically, kernel ridge regression is used to approximate the kinetic energy of non-interacting fermions confined to a 1D box as a functional of electron density. This machine-learned density functional approximation achieves highly accurate energies and self-consistent densities, outperforming traditional approximations. Various kernels, cross-validation methods, and representations of the electron density are explored to optimize the machine-learned approximation.
Computational chemistry uses numerical simulations based on the laws of physics to model chemical structures and reactions. There are different types of computational models of varying accuracy and computational cost, including molecular mechanics, semi-empirical, ab initio, and density functional theory methods. The accuracy of calculations also depends on the basis set used to describe molecular orbitals. Computational chemistry has become an important tool for characterizing nanomaterials.
This document provides an introduction to computational quantum chemistry. It defines computational chemistry as using mathematical approximations and computer programs to solve chemical problems based on quantum mechanics. Specifically, computational quantum chemistry focuses on solving the Schr旦dinger equation for molecular systems using approximations like the Born-Oppenheimer approximation. It discusses how computational methods can be used to calculate various molecular properties and motivates the need for approximations due to the inability to exactly solve the Schr旦dinger equation for complex molecules. The document then provides an overview of common computational methods like Hartree-Fock, configuration interaction, M淡ller-Plesset perturbation theory, and coupled cluster theory.
This document provides an introduction to computational quantum chemistry. It defines computational chemistry as using mathematical approximations and computer programs to solve chemical problems based on quantum mechanics. Specifically, computational quantum chemistry focuses on solving the Schr旦dinger equation for molecular systems using approximations like the Born-Oppenheimer approximation. It also discusses methods for approximating the wavefunction like Hartree-Fock, configuration interaction, and density functional theory as well as expanding the molecular orbitals in a basis set of atomic orbitals.
The document summarizes the hopping mechanism for charge transport in organic materials. It describes how charge transport occurs through hopping between molecules, with the hopping rate determined by Marcus electron transfer theory. It discusses methods to calculate key parameters in the Marcus rate formula, including the transfer integral between molecules and the reorganization energy, using first-principles density functional theory calculations. These parameters and the Marcus rate theory can then be used to simulate charge mobility through random walk models.
Molecular and Quantum Mechanics in drug designAjay Kumar
油
This document discusses and compares molecular mechanics and quantum mechanics methods for drug design. It provides an overview of molecular mechanics, which uses classical physics to model potential energy surfaces, and common molecular mechanics force fields such as AMBER and CHARMM. It also describes quantum mechanics principles, density functional theory, and semi-empirical methods. Key differences between molecular mechanics and quantum mechanics are noted, such as system size, time required, and accuracy. Applications of each method in drug design are mentioned.
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1. 1
Density Functional Theory (DFT)
1998: Nobel prize awarded to Walter Kohn
Walter Kohn a Austrian-
American theoretical physicist
and theoretical chemist
To solve many body problems by Schr旦dinger's
equation.
H = E
2. 2
Working of DFT
Only up to one
electron problem we
can solve
Schrodinger equation
exactly
We have to involve
some approximations
(BORN
OPENHEIMER
APPROXIMATION)
Hohenberg kohn
theorem
We shall use the
electron density
as a functional
Then we shall
calculate
ground state
properties
3. The Hamiltonian for N-Particle system
BORN OPPENHEIMER APPROXIMATION
Reduced dimension from 3Ne to 3 by considering nuclei is static.
HOHENBERG KOHN THEOREMS
Theorem: 1
The external potential
vext or the ground state
energy E is a unique
functional of electron
density.
Theorem: 2
The electron density that
minimizes the energy of
the overall functional is
the true ground state
electron density.
Limitations of HK
Theorems
They do provide method
of finding in practice
however these theorems
were not very helpful in
real calculation. Two
other scientists Kohn
and Sham gave an
equation which turned
DFT into a practical
tool.
17
4. *KOHN- SHAM SCHEME
4
The K-S equations turned the DFT one of the practical tool for the
calculation of ground state density.
The Kohn-Sham total energy equation is,
kinetic energy
of electrons
External
potential due
to nucleus
Coulomb
energy due to
electron-
electron
interaction
Exchange and
correlation
energy term
No theoretical tool
is determined and
hence
approximations are
needed to calculate
this energy
5. 5
* Approximations
Local density
approximation
(LDA)
Generalized Gradient
Approximation
(GGA)
It gives more appropriate
result for that material
whose density will be same
during reactions.
GGA is improved form of LDA. In
GGA Exc in any microscopic
volume not only depend on the
local density in that volume but
on the density of adjacent
volumes as well. By using
gradient of density together with
constant density calculations of
LDA can be improved.
6. 6
*Amsterdam Density Functional
Developed in 1970
Vrije University of Amsterdam and university of Calgary ,
Canada.
Structure, Reactivity and spectra of molecules.
Transition metal complexes and molecules with heavy atoms.
20
7. Computational Detail
Structure is build from Space Group Fm-3m (no.225)
Lattice Parameters a= 5.984
Miller Indices of (001) is used to cut slab from bulk.
Geometry Optimization
LDA
GGA-mPBE approximation is used.
7
#1: DFT is a computational quantum mechanical modelling method used.
DFT is used for the calculation of electric, magnetic, structural and different other properties of solids.
Only up to one electron problem we can solve Schr旦dinger's equation exactly. It is very hectic to solve the Schr旦dinger's equation for a N- body system. In DFT instead of electronic wave function, ground state electron density (r), is used to solve many body problems.
#2: Electron density油is a representation of the probability of finding an油electron油in a specific location around an atom or molecule. In general, the油electron油is more likely to be found in regions with high油electron density. Density of electron (r) only depends upon the three coordinates of position x, y and z instead of 3N-coordinates.
#3: There are bunch of nuclei and electrons, making the
equation very difficult to solve
#4: The nuclear attraction energy part of the electronic Hamiltonian operator is called external potential
The part of the binding油energy油of a system of particles, such as an atomic nucleus of a solid, which is associated with electrostatic forces between the particles.
Exchange correlation energy: sum of energy of interacting system.
Correlation energy: interaction energy of electrons with different spin.
Spin effect of electrons as well as their interaction was included.
#5: The exchange correlation energy at any point gives the same value to that of uniform electron gas for identical density of a system.
Taking the gradient of electron density improvements enhanced the accuracy of results upto large extent compared to LDA.
#6: Amsterdam Density Functional (ADF) is particularly strong油in油understanding油and predicting.
ADF is frequently used for studying
since all elements in the periodic table can be modeled accurately
#7: (Generalized Gradient Approximation-modified Perdew Burke Erenzerhof
The lattice parameters are the quantities specifying a unit cell or the unit of the periodicity of the atomic arrangement. The lattice parameters (constants) are composed of "a, b, c," lengths of the unit cell in three dimensions, and "留, 硫, 粒," their mutual angles.
Miller indices, group of three numbers that indicates the orientation of a plane or set of parallel planes of atoms in a crystal.