InstitutefürComputerphysikUniversitätStuttgart MariaFyta Simulationstechnik

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Simulationstechnik

Maria Fyta

Institute für Computerphysik Universität Stuttgart

3. Mai 2017

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Course contents

Quantum-mechanical methods Electronic structure calculations ab initioMolecular Dynamics More accurate schemes

Pair-potentials (materials modeling) Hydrodynamic methods lattice-Boltzmann Free energy methods

(Coarse-grained models and multi-scale methods)

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Recommended Literature

D. Frenkel and B. Smit,Understanding Molecular Simulation, Academic Press, San Diego, 2002.

M.P. Allen and D.J. Tildesley,Computer Simulation of Liquids, Oxford Science Publications, Clarendon Press, Oxford, 1987.

D. C. Rapaport,The Art of Molecular Dynamics Simulation, Cambridge University Press, 2004.

D. P. Landau and K. Binder,A guide to Monte Carlo Simulations in Statistical Physics, Cambridge, 2005.

J.M. Thijssen,Computational Physics, Cambridge (2007)

S. Succi,The Lattice Boltzmann Equation for Fluid Dynamics and Beyond, Oxford Science Publ. (2001).

A. Leach,Molecular Modelling: Principles and Applications, Pearson Education Ltd. (2001).

R.M. Martin,Electronic Stucture, Basic Theory and Practical Methods, Cambridge (2004).

E. Kaxiras,Atomic and electronic structure of solids, Cambridge (2003).

http://www.icp.uni-stuttgart.de Maria Fyta 3/17

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Why Simulations?

Insights into highly complex systems and scales often not accessible with experiments.

Materials properties (e.g. strength, stability, optical response, etc.) Chemical reactions

Biological functions (protein folding) Weather forecast

Traffic simulations etc. ...

- dynamic properties [time-dependent] (diffusion behavior, chemical reactions, etc.)

- static properties [time-independent] (thermodynamic properties, fracture, etc.)

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Computational Modeling Bridge theory and experiments Verify or guide experiments

Involves different spatial and temporal scales

Extraction of a wider range of properties, mechanical, chamical, thermodynamic, optical, electronic, etc...

Accuracy vs. Efficiency!

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Typical time/length scales

10⁻¹¹–10⁻⁹m/1fs–100ps: chemical reactions, hydrogen bonds,

electronic properties [evaluation of the electronic degrees of freedom.]

10⁻¹⁰–10⁻⁷m/1ps–1µs: diffusion processes, folding of small proteins [classical description, no quantum effects, nuclei degrees of freedom]

10⁻⁸–10⁻⁴m/1µs–100ms: fracture processes, polymer relaxation, colloids [omit ’fast’ degrees of freedom, coarse-grained description, mesoscopic simulations]

Typical particle (electrons, atoms, coarse-grained beads) numbers in the simulations: 100–10⁷→computational time increases with complexity of the system (more accurate description of large system size)

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Introduction to electronic structure

Source: commons.wikimedia.org

Different properties according to atom type and number, i.e. number of electrons and their spatial and electronic configurations.

Ground state: The configuration that corresponds to the lowest electronic energy.

Excited state: Any other configuration.

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Periodic table of the elements

Source: chemistry.about.com

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Electronic structure

Probability distribution of electrons in chemical systems

State of motion of electrons in an electrostatic field created by the nuclei Extraction of wavefunctions and associated energies through the Schrödinger equation:

i~∂

∂tΨ = ˆHΨ time−dependent

EΨ = ˆHΨ time−independent

Solves for:

bonding and structure,

electronic, magnetic, optical, thermal, ... properties of systems, chemistry and reactions.

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Time-independent molecular Schrödinger equation ( ˆT_e+V_ee+V_ek+ ˆT_k +V_kk)Ψ(r,R) =EΨ(r,R)

r, R: electron, nucleus coordinates

Tˆe,Tˆ_k: electron, nucleus kinetic energy operator Vee: electron-electron repulsion

V_ek: electron-nuclear attraction V_kk: nuclear-nuclear repulsion E: total molecular energy

Ψ(r,R): total molecular wavefunction Born-Oppenheimer approximation

Electrons much faster than nuclei→separate nuclear from electronic motion

Solve electronic and nuclear Schrödinger equation,Ψe(r;R)andΨk(r)

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Approximations in electronic structure methods Common approximations:

in the Hamiltonian, e.g. changing from a wavefunction-based to a density-based description of the electronic interaction

simplification of the electronic interaction term in the description of the many-electron wavefunction

Often the electronic wavefunction of a system is expanded in terms of Slater determinants, as a sum of anti-symmetric electron wavefunctions:

Ψ_el(r~₁,s₁, ~r₂,s₂, ..., ~r_N,s_N) = X

m1,m2,...,mN

C_m₁_,m₂_,...,m_N|φ_m₁(r~₁,s₁)φ_m₂(~r₂,s₂)...φ_m_N(r~_N,s_N)|

where~r_i,s₁the cartesian coordinates and the spin components. The componentsφmN(r~_N,s_N)are one-electron orbitals.

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Basis-sets

wavefunctions represented as vectors

compontents of vectors correspond to coefficients related to basis-set basis-sets: set of functions combined (typically in linear combinations) to create the wavefunctions of the system

Finite basis set ; computation: always an approximation

The smaller the basis, the poorer the representation, i.e. accuracy of results

The larger the basis, the larger the computational load.

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Basis-sets Plane-waves

Periodic functions

Bloch’s theorem for periodic solids:φm_N(r~_N,s_N) =u_n,k(~r)exp(i~k·~r) Periodicuexpanded in plane waves with expansion coefficients depending on the reciprocal lattice vectors:

u_n,k(~r) = X

|~G|≤|Gmax

c_nk(G)exp(i~ G~ ·~r)

Atomic-like orbitals φ_m_N(r~_N,s_N) =P

nD_nmχ_n(~r) Gaussian-type orbitals:

χ_ζ,n,l,m(r, θ, φ) =NY_l,m(θ, φ)r^2n−2−lexp(−ζr²)

χ_ζ,l_x_,l_y_,l_z(x,y,z) =Nx^l^xy^l^yz^l^zexp(−ζr²) the suml_x,l_y,l_z determines the orbital.

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Optimization: wavefunctions and geometries

numerical approximations of the wavefunction by successive iterations variational principle, convergence by minimizing the total energy:

E ≤ hΦ|H|Φi

geometry optimization: nuclear forces computed at the end of wavefunction optimization process

nuclei shifted along direction of computed forces→new wavefunction(new positions)

process until convergence: final geometry corresponds to global minimum of potential surface energy

Self consistent field (SCF)

Particles in the mean field created by the other particles

Final field as computed from the wavefunction or charge density is self-consistentwith the assumed initial field

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ab initiomethods

Solve Schrödinger’s equation associated with the Hamiltonian of the system

ab initio(first-principles): methods which use established laws of physics and do not include empirical or semi-empirical parameters;

derived directly from theoretical principles, with no inclusion of experimental data

Popularab initiomethods Hartree-Fock

(Density functional theory)

Møller-Plesset perturbation theory

Multi-configurations self consistent field (MCSCF)

Configuration interaction (CI), Multi-reference configuration interaction Coupled cluster (CC)

Quantum Monte Carlo

Reduced density matrix approaches