Presentation is loading. Please wait. # Modelling of Defects DFT and complementary methods

## Presentation on theme: "Modelling of Defects DFT and complementary methods"— Presentation transcript:

Modelling of Defects DFT and complementary methods
Tor Svendsen Bjørheim PhD fellow, FERMiO, Department of Chemistry University of Oslo NorFERM-2008, 3rd -7th of October 2008

Outline Background / Introduction DFT Supplementary Methods Summary
Theory DFT calculations in practice Case studies Supplementary Methods Nudged Elastic Band Molecular Dynamics Monte Carlo approach Summary

Introduction to defect modeling
Experimental techniques Time consuming Expensive Inconclusive results Supplement with computational studies Condensed systems Mutually interacting particles Described by the full Hamiltonian: Increasingly complex with larger number of electrons (3N variables) Need simplifications! Hartree-Fock DFT

DFT - Density Functional Theory

Density Functional Theory
Ab initio ground state theory Basic variable electron density, n(r) n(r) depends only on the three spatial variables Hohenberg-Kohn theorems (1964): For a system of interacting particles in an external potential, the external potential and hence the total energy is a unique functional of n(r) The ground state energy can be obtained variationally; the density that minimizes the total energy is the exact ground state density Problem: kinetic energy of the electrons… Practical use: Kohn-Sham approach (1965) Introduce a reference system of non-interacting particles (with the same n(r)) Kinetic energy easily determined: Functional = Function of a function

Need to determine the orbitals of the reference system
Total energy: Exc: Need to determine the orbitals of the reference system Kohn-Sham equations: Kohn-Sham potential: Depends on the electron density  Can not be solved directly Self consistent solutions using iterative schemes Kohn-Sham approach In principle exact Do not know the form of EXC  Approximate Modern DFT: new and improved EXC

Exchange-Correlation Functionals
Simplest approach: Local Density Approximations - LDA Assume Exc equal to Exc in a homogeneous electron gas: Locally constant electron density Systems with slowly varying electron densities Inadequate for systems with quickly varying electron densities Improvements: Generelized-Gradient Approximations (GGA) Include gradients of the electron density at each point E.g. GGA-PW91 and GGA-PBE Problems: Band gaps and Van der Waals forces…..

DFT calculations in practice
Real solids ~1023 atoms Huge number of wave functions.. Need further simplifications! Popular approach: plane waves Periodically repeating unit cell Bloch’s theorem: Finite number of wave functions over an infinite number of k-points in the 1. Brillouin Zone! K-point sampling Electronic states at a finite number of k-points Finite number of wave functions at a finite number of k-points in the 1. Brillouin Zone! K-mesh needs to be chosen carefully

Defects in solids Solids with one or more defects Supercell method
Aperiodic systems Bloch’s theorem can not be applied Can not use plane wave basis sets Introduce the concept of a periodically repeating supercell Supercell method Defects in a ’box’ consisting of n unit cells Define the supercell with the defect as the new unit cell Periodic boundaries 3D periodic ordering of defects Typical size: <500 atoms Surfaces/interfaces & molecules

Alternative approaches
Finite-Cluster approach Defects in finite atomic clusters No interaction with defects in neighboring unit cells Avoid surface effects: large clusters Green’s Function Embedding Technique Purely mathematical Defect regions embedded in known DFT Green’s function of bulk Ideal for studies of isolated defects (in theory) Numerically challenging…..

Structural studies Structural studies Defect positions
Locate global minimum E.g. proton positions Local arrangement around isolated defects E.g. local displacements in ferroelectrics

Thermodynamics Formation energy of isolated point defects:
Defects that change the composition: Total energies and a set of chemical potentials E.g. isolated protonic defects & oxygen vacancies: Formation: Formation energy: Atmospheric conditions Thermodynamic tables - Supercell size (unit cells) Effective defect concentration Fermi level:

Formation energies not directly comparable with experimental results
DFT = ground state = 0 K! Chemical potential of the electrons Combine to e.g. hydration: E.g. hydration of AZrO3 perovskites Increasing stability in orthorhombic perovskites ∆Hhydr(DFT) reproduce experimental trends (eV) (kJ/mol) CaZrO3 -1.79 0.46 -147 SrZrO3 -1.54 0.69 -120 -106.1 PbZrO3 -1.32 0.65 -75 -103 BaZrO3 -1.22 0.76 -66 -80

Defect levels Defect levels Transition between charge states: Ef(q/q’)
Experimentally: DLTS Determine the preferred charge state of defects In supercell calculations: Determine ΔEf for all charge states ΔEf for all Fermi level positions Most stable: charge state with lowest ΔEf

Hydrogen in semiconductors
Possibilities: Protons, neutral hydrogen & hydride ions Transition levels ε(0/-), ε(+/0), ε(+/-) ZnO  ε(+/-) above CBM H: shallow donor Only stable LaNbO4  ε(+/-) 1.96 eV below CBM favorable at high Fermi levels n-type LaNbO4: dominated conductivity?  C.G. Van de Walle and J. Neugebauer, Nature (2003), 423  A. Kuwabara, Private Communication, (2008)

Overall dominating defect (concentration-wise)
Need to calculate all possible defects LaNbO4  Interstitial oxygen dominates at high Fermi levels (0 K!)  A. Kuwabara, Private Communication, (2008)

When DFT fails… / eV In defect calculations: band gaps
Heavily underestimated Both LDA/GGA functionals Affects: Defect levels Defect formation energies Correction: scissor operation! Shift the conduction band Align Eg(DFT) and Eg(EXP) Donor type defects follow CB Correct formation energies Alternative: hybrid or semi-empirical functionals / eV BaZrO3 3.2 5.3 PbZrO3 3.0 3.7 ZnO 1.9 3.4

Complementary methods

Nudged Elastic Band Method
Method for studying transition states: Saddle points Minimum Energy Paths (MEP) Know initial and final states Local/global minima Obtained by total energy calculations (DFT) Reaction path Divided into ’images’ (< 20) Image = ‘ snapshots’ between initial and final states Equidistant images - connected by ’springs’ Optimize each image (DFT+forces)  MEP Chosen images not saddle points  interpolate

Proton transport in LaPO4 
Proton transport in LaPO4 using NEB+DFT (VASP) Saddle points: Jump rates: Determine dominating transport mechanism Diffusion and conductivity: Proton transport processes: Rotation around oxygen Oscillatory motion Intra-tetrahedral jumps Inter-tetrahedral jumps  R. Yu and L. C. De Jonghe, J. Phys. Chem. C 111 (2007) 11003

Intratetrahedral jumps:
High energy barrier Intertetrahedral jumps: Two tetrahedrons Lower energy barrier Intertetrahedral jumps: Three tetrahedrons Even lower energy barrier Experimentally: eV

Molecular Dynamics Used to simulate time evolution of classical many-particle systems Obey the laws of classical mechanics Condensed systems: Classical particles moving under influence of an interaction potential, V(R1,…,RN) Forces on the ions: MD algorithms: Discretize equation of motion Trajectories: stepwise update positions and velocities

Interionic interactions:
Model potential vs. first-principles Model potential Parameterized to fit experimental or first principles data Advantages Possible to treat large systems Long time evolution Disadvantages Inaccurate potentials  Poor force representation First-principles First-principles electronic structure calculation (e.g. DFT) at each ionic step Advantages: Accurate forces Realistic dynamic description Disadvatages Computationally demanding Small systems (~100 ions) Short time periods (~ps) Good statistical accuracy Poor statistical accuracy..

Monte-Carlo approach Proton diffusion Loosely described:
Statistical simulation methods Conventional methods (MD): Discretize equations describing the physical process E.g. equations of motion Monte-Carlo approach Simulate the physical process directly No need to solve the underlying equations Requirement: process described by probability distribution functions (PDF) Average results over the number of observations Proton diffusion Jump frequency and PDF:

Summary Many different computational approaches Systematic trends
Fundamental processes Predict defect properties of real materials Combine different methods

Colleagues at the Solid State Electrochemistry group in Oslo
Acknowledgements Akihide Kuwabara Espen Flage-Larsen Svein Stølen Truls Norby Colleagues at the Solid State Electrochemistry group in Oslo Thank You !!

Thank You!

Download ppt "Modelling of Defects DFT and complementary methods"

Similar presentations

Ads by Google