Energy and Forces in DFT

Size: px
Start display at page:

Download "Energy and Forces in DFT"

Transcription

1 Energy and Forces in DFT Total Energy as a function of nuclear positions {R} E tot ({R}) = E DF T ({R}) + E II ({R}) (1) where E DF T ({R}) = DFT energy calculated for the ground-state density charge-density n(r) Hellmann-Feynman forces: F i = de = dr i V (r) n(r) dr E II (2) R i R i where V {R} = external potential acting on electrons

2 DFT and Molecular Dynamics Let us assume i) classical nuclei, ii) electrons in the ground state. We can introduce a classical Lagrangian: L = 1 2 i M i Ṙ 2 i E tot ({R}) (3) describing the motion of nuclei. Equations of motion: d L dt Ṙi L R i = 0, P i = L Ṙi (4) are nothing but usual Newton s equations: P i M i V i, M i V i = F i (5)

3 Discretization of the equation of motion Verlet algorithm: Velocity Verlet: R i (t + δt) = 2R i (t) R i (t δt) + δt2 M i F i (t) + O(δt 4 ) (6) V i (t) = 1 2δt [R i(t + δt) R i (t δt)] + O(δt 3 ). (7) V i (t + δt) = V i (t) + δt 2M i [F i (t) + F i (t + δt)] (8) R i (t + δt) = R i (t) + δtv i (t) + δt2 2M i F i (t). (9) Both sample the microcanonical ensemble, or NVE: the energy is conserved.

4 Thermodynamical averages Averages (ρ = probability of a microscopic state): A = ρ(r, P)A(R, P)dRdP (10) are usually well approximated by averages over time: and the latter by discrete average over a trajectory: lim A T A (11) T A T = 1 T T 0 A(R(t), P(t))dt 1 M M n=1 A(t n ), t n = nδt, t M = Mδt = T. (12)

5 Costant-Temperature and constant-pressure dynamics Molecular Dynamics with Nosé Thermostats samples the canonical ensemble (NVT): the average temperature is fixed N i=1 P 2 i 2M i NV T = 3 2 Nk BT (13) Molecular Dynamics with variable cell samples the NPT ensemble: the average pressure is fixed In both cases, additional (fictitious) degrees of freedom are considered

6 Technicalities time step as big as possible, but small enough to follow nuclear motion with little loss of accuracy. Rule of thumb: δt δt max, where δt max = 1/ω max = period of the fastest phonon mode. calculations of forces must be very accurate at each time step (good self-consistency needed) or else a systematic energy drift will appear Note that: error for DFT energy is quadratic in the SCF error of the charge density error for DFT forces is linear in the SCF error of the charge density

7 Car-Parrinello Molecular Dynamics The idea: introduce a (fictitious) electron dynamics that keeps the electrons close to the ground state the electron dynamics is faster than the nuclear dynamics and averages out the error......but not too fast so that a reasonable time step can be used Very simple and effective! All classical MD technology can be used Requires a judicious choice of simulation parameters

8 Car-Parrinello Lagrangian L = µ dr 2 ψ k (r) M i Ṙ 2 i E tot ({R}, {ψ}) 2 k i + ( ) Λ kl ψk(r)ψ l (r)dr δ kl k,l (14) generates equations of motion: µ ψ k = Hψ k l Λ kl ψ l, M i Ri = E tot R i (15) µ = fictitious electronic mass Λ kl = Lagrange multipliers enforce orthonormality constraints.

9 Car-Parrinello Lagrangian (2) electronic degrees of freedom ψ k orbitals into PW are the expansion coefficients of KS forces on electrons are determined by the KS Hamiltonian calculated from current values of ψ k and of R i forces acting on nuclei have the Hellmann-Feynman form: E tot R i = k ψ k V R i ψ k (16) but they slightly differ from true forces

10 Car-Parrinello technicalities Starting point: bring the electrons to the ground state at fixed ions The simulation is performed using classical MD technology (Verlet) on both nuclear positions and electronic degrees of freedom Orthonormality constraints are exactly imposed at each time step, using an iterative procedure The choice of parameters (fictitious electronic mass µ and time step δt) must ensure that there is no energy transfer from nuclei to electrons, i.e. the kinetic energy of the electronic degrees of freedom does not systematically increase

11 Choice of the parameters The fictitious electronic mass µ and time step δt must be chosen in such a way that: µ is be big enough to enable the use of a reasonable time step, small enough to guarantee adiabaticity, i.e. no energy transfer from nuclei to electrons, which always remain close to the ground state correctness of the nuclear trajectory Typical values: electron masses δt should be the largest value that yields a stable dynamics (no drifts, no loss of orthonormality)

12 Why Car-Parrinello works The CP dynamics is classical, both for nuclei and electrons: the energy should equipartition! Typical frequencies associated to electron dynamics: ω el (ɛ i ɛ j )/µ. if there is a gap in the electronic spectrum, ω el min ɛ gap /µ Typical frequencies associated to nuclear dynamics: phonon frequencies ω ph If ω ph max << ω el min there is negligible energy transfer from nuclei to electrons A fast electron dynamics keeps the electrons close to the ground state and averages out the error on the forces. The slow nuclear dynamics is very close to the correct one, i.e. with electrons in the ground state. (Pastore, Smargiassi, Buda, PRA 44, 6334, 1991).

Ab initio molecular dynamics. Simone Piccinin CNR-IOM DEMOCRITOS Trieste, Italy. Bangalore, 04 September 2014

Ab initio molecular dynamics. Simone Piccinin CNR-IOM DEMOCRITOS Trieste, Italy. Bangalore, 04 September 2014 Ab initio molecular dynamics Simone Piccinin CNR-IOM DEMOCRITOS Trieste, Italy Bangalore, 04 September 2014 What is MD? 1) Liquid 4) Dye/TiO2/electrolyte 2) Liquids 3) Solvated protein 5) Solid to liquid

More information

Ab initio molecular dynamics

Ab initio molecular dynamics Ab initio molecular dynamics Molecular dynamics Why? allows realistic simulation of equilibrium and transport properties in Nature ensemble averages can be used for statistical mechanics time evolution

More information

MD Thermodynamics. Lecture 12 3/26/18. Harvard SEAS AP 275 Atomistic Modeling of Materials Boris Kozinsky

MD Thermodynamics. Lecture 12 3/26/18. Harvard SEAS AP 275 Atomistic Modeling of Materials Boris Kozinsky MD Thermodynamics Lecture 1 3/6/18 1 Molecular dynamics The force depends on positions only (not velocities) Total energy is conserved (micro canonical evolution) Newton s equations of motion (second order

More information

Ab initio molecular dynamics: Basic Theory and Advanced Met

Ab initio molecular dynamics: Basic Theory and Advanced Met Ab initio molecular dynamics: Basic Theory and Advanced Methods Uni Mainz October 30, 2016 Bio-inspired catalyst for hydrogen production Ab-initio MD simulations are used to learn how the active site

More information

Ab initio Molecular Dynamics Born Oppenheimer and beyond

Ab initio Molecular Dynamics Born Oppenheimer and beyond Ab initio Molecular Dynamics Born Oppenheimer and beyond Reminder, reliability of MD MD trajectories are chaotic (exponential divergence with respect to initial conditions), BUT... With a good integrator

More information

Ab initio molecular dynamics and nuclear quantum effects

Ab initio molecular dynamics and nuclear quantum effects Ab initio molecular dynamics and nuclear quantum effects Luca M. Ghiringhelli Fritz Haber Institute Hands on workshop density functional theory and beyond: First principles simulations of molecules and

More information

Time reversible Born Oppenheimer molecular dynamics

Time reversible Born Oppenheimer molecular dynamics Time reversible Born Oppenheimer molecular dynamics Jianfeng Lu Mathematics Department Department of Physics Duke University jianfeng@math.duke.edu KI-Net Conference, CSCAMM, University of Maryland, May

More information

Accelerated Quantum Molecular Dynamics

Accelerated Quantum Molecular Dynamics Accelerated Quantum Molecular Dynamics Enrique Martinez, Christian Negre, Marc J. Cawkwell, Danny Perez, Arthur F. Voter and Anders M. N. Niklasson Outline Quantum MD Current approaches Challenges Extended

More information

Ab initio molecular dynamics : BO

Ab initio molecular dynamics : BO School on First Principles Simulations, JNCASR, 2010 Ab initio molecular dynamics : BO Vardha Srinivasan IISER Bhopal Why ab initio MD Free of parametrization and only fundamental constants required. Bond

More information

Next generation extended Lagrangian first principles molecular dynamics

Next generation extended Lagrangian first principles molecular dynamics Next generation extended Lagrangian first principles molecular dynamics Anders M. N. Niklasson Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545 (Dated: June 1, 017) LA-UR-17-3007

More information

Ab Ini'o Molecular Dynamics (MD) Simula?ons

Ab Ini'o Molecular Dynamics (MD) Simula?ons Ab Ini'o Molecular Dynamics (MD) Simula?ons Rick Remsing ICMS, CCDM, Temple University, Philadelphia, PA What are Molecular Dynamics (MD) Simulations? Technique to compute statistical and transport properties

More information

What is Classical Molecular Dynamics?

What is Classical Molecular Dynamics? What is Classical Molecular Dynamics? Simulation of explicit particles (atoms, ions,... ) Particles interact via relatively simple analytical potential functions Newton s equations of motion are integrated

More information

Molecular Dynamics Simulations

Molecular Dynamics Simulations Molecular Dynamics Simulations Dr. Kasra Momeni www.knanosys.com Outline Long-range Interactions Ewald Sum Fast Multipole Method Spherically Truncated Coulombic Potential Speeding up Calculations SPaSM

More information

Javier Junquera. Statistical mechanics

Javier Junquera. Statistical mechanics Javier Junquera Statistical mechanics From the microscopic to the macroscopic level: the realm of statistical mechanics Computer simulations Thermodynamic state Generates information at the microscopic

More information

Computer simulation methods (1) Dr. Vania Calandrini

Computer simulation methods (1) Dr. Vania Calandrini Computer simulation methods (1) Dr. Vania Calandrini Why computational methods To understand and predict the properties of complex systems (many degrees of freedom): liquids, solids, adsorption of molecules

More information

Introduction Statistical Thermodynamics. Monday, January 6, 14

Introduction Statistical Thermodynamics. Monday, January 6, 14 Introduction Statistical Thermodynamics 1 Molecular Simulations Molecular dynamics: solve equations of motion Monte Carlo: importance sampling r 1 r 2 r n MD MC r 1 r 2 2 r n 2 3 3 4 4 Questions How can

More information

Statistical Mechanics. Atomistic view of Materials

Statistical Mechanics. Atomistic view of Materials Statistical Mechanics Atomistic view of Materials What is statistical mechanics? Microscopic (atoms, electrons, etc.) Statistical mechanics Macroscopic (Thermodynamics) Sample with constrains Fixed thermodynamics

More information

Ab initio Molecular Dynamics

Ab initio Molecular Dynamics Ab initio Molecular Dynamics Jürg Hutter Department of Chemistry, University of Zurich Overview Equations of motion and Integrators General Lagrangian for AIMD Car Parrinello MD and Born Oppenheimer MD

More information

Modeling of Nanostructures and Materials Jacek A. Majewski Summer Semester 2013 Lecture 6 Lecture Modeling of Nanostructures Molecular Dynamics

Modeling of Nanostructures and Materials Jacek A. Majewski Summer Semester 2013 Lecture 6 Lecture Modeling of Nanostructures Molecular Dynamics Chair of Condensed Matter Physics nstitute of Theoretical Physics Faculty of Physics, Universityof Warsaw Summer Semester 013 Lecture Modeling of Nanostructures and Materials Jacek A. Majewski E-mail:

More information

Gear methods I + 1/18

Gear methods I + 1/18 Gear methods I + 1/18 Predictor-corrector type: knowledge of history is used to predict an approximate solution, which is made more accurate in the following step we do not want (otherwise good) methods

More information

A Nobel Prize for Molecular Dynamics and QM/MM What is Classical Molecular Dynamics? Simulation of explicit particles (atoms, ions,... ) Particles interact via relatively simple analytical potential

More information

Ab Initio Molecular Dynamic

Ab Initio Molecular Dynamic Ab Initio Molecular Dynamic Paul Fleurat-Lessard AtoSim Master / RFCT Ab Initio Molecular Dynamic p.1/67 Summary I. Introduction II. Classical Molecular Dynamic III. Ab Initio Molecular Dynamic (AIMD)

More information

Analysis of MD Results Using Statistical Mechanics Methods. Molecular Modeling

Analysis of MD Results Using Statistical Mechanics Methods. Molecular Modeling Analysis of MD Results Using Statistical Mechanics Methods Ioan Kosztin eckman Institute University of Illinois at Urbana-Champaign Molecular Modeling. Model building. Molecular Dynamics Simulation 3.

More information

Density Functional Theory: from theory to Applications

Density Functional Theory: from theory to Applications Density Functional Theory: from theory to Applications Uni Mainz May 27, 2012 Large barrier-activated processes time-dependent bias potential extended Lagrangian formalism Basic idea: during the MD dynamics

More information

First-principles Molecular Dynamics Simulations

First-principles Molecular Dynamics Simulations First-principles Molecular Dynamics Simulations François Gygi University of California, Davis fgygi@ucdavis.edu http://eslab.ucdavis.edu http://www.quantum-simulation.org MICCoM Computational School, Jul

More information

7 To solve numerically the equation of motion, we use the velocity Verlet or leap frog algorithm. _ V i n = F i n m i (F.5) For time step, we approxim

7 To solve numerically the equation of motion, we use the velocity Verlet or leap frog algorithm. _ V i n = F i n m i (F.5) For time step, we approxim 69 Appendix F Molecular Dynamics F. Introduction In this chapter, we deal with the theories and techniques used in molecular dynamics simulation. The fundamental dynamics equations of any system is the

More information

A Brief Introduction to Statistical Mechanics

A Brief Introduction to Statistical Mechanics A Brief Introduction to Statistical Mechanics E. J. Maginn, J. K. Shah Department of Chemical and Biomolecular Engineering University of Notre Dame Notre Dame, IN 46556 USA Monte Carlo Workshop Universidade

More information

Ab Initio Molecular Dynamic

Ab Initio Molecular Dynamic Ab Initio Molecular Dynamic Paul Fleurat-Lessard AtoSim Master Ab Initio Molecular Dynamic p.1/67 Summary I. Introduction II. Classical Molecular Dynamic III. Ab Initio Molecular Dynamic (AIMD) 1 - Which

More information

Molecular Dynamics. Park City June 2005 Tully

Molecular Dynamics. Park City June 2005 Tully Molecular Dynamics John Lance Natasa Vinod Xiaosong Dufie Priya Sharani Hongzhi Group: August, 2004 Prelude: Classical Mechanics Newton s equations: F = ma = mq = p Force is the gradient of the potential:

More information

1. Thermodynamics 1.1. A macroscopic view of matter

1. Thermodynamics 1.1. A macroscopic view of matter 1. Thermodynamics 1.1. A macroscopic view of matter Intensive: independent of the amount of substance, e.g. temperature,pressure. Extensive: depends on the amount of substance, e.g. internal energy, enthalpy.

More information

Classical Molecular Dynamics

Classical Molecular Dynamics Classical Molecular Dynamics Matt Probert Condensed Matter Dynamics Group Department of Physics, University of York, U.K. http://www-users.york.ac.uk/~mijp1 Overview of lecture n Motivation n Types of

More information

CHE3935. Lecture 4 Quantum Mechanical Simulation Methods Continued

CHE3935. Lecture 4 Quantum Mechanical Simulation Methods Continued CHE3935 Lecture 4 Quantum Mechanical Simulation Methods Continued 1 OUTLINE Review Introduction to CPMD MD and ensembles The functionals of density functional theory Return to ab initio methods Binding

More information

Density Functional Theory: from theory to Applications

Density Functional Theory: from theory to Applications Density Functional Theory: from theory to Applications Uni Mainz May 14, 2012 All electrons vs pseudopotentials Classes of Basis-set Condensed phase: Bloch s th and PBC Hamann-Schlüter-Chiang pseudopotentials

More information

G : Statistical Mechanics

G : Statistical Mechanics G25.2651: Statistical Mechanics Notes for Lecture 1 Defining statistical mechanics: Statistical Mechanics provies the connection between microscopic motion of individual atoms of matter and macroscopically

More information

Citation for published version (APA): Hess, B. (2002). Stochastic concepts in molecular simulation Groningen: s.n.

Citation for published version (APA): Hess, B. (2002). Stochastic concepts in molecular simulation Groningen: s.n. University of Groningen Stochastic concepts in molecular simulation Hess, Berk IMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite from it. Please check

More information

Temperature and Pressure Controls

Temperature and Pressure Controls Ensembles Temperature and Pressure Controls 1. (E, V, N) microcanonical (constant energy) 2. (T, V, N) canonical, constant volume 3. (T, P N) constant pressure 4. (T, V, µ) grand canonical #2, 3 or 4 are

More information

Enabling constant pressure hybrid Monte Carlo simulations using the GROMACS molecular simulation package

Enabling constant pressure hybrid Monte Carlo simulations using the GROMACS molecular simulation package Enabling constant pressure hybrid Monte Carlo simulations using the GROMACS molecular simulation package Mario Fernández Pendás MSBMS Group Supervised by Bruno Escribano and Elena Akhmatskaya BCAM 18 October

More information

Algorithms and Computational Aspects of DFT Calculations

Algorithms and Computational Aspects of DFT Calculations Algorithms and Computational Aspects of DFT Calculations Part I Juan Meza and Chao Yang High Performance Computing Research Lawrence Berkeley National Laboratory IMA Tutorial Mathematical and Computational

More information

Quantum Molecular Dynamics Basics

Quantum Molecular Dynamics Basics Quantum Molecular Dynamics Basics Aiichiro Nakano Collaboratory for Advanced Computing & Simulations Depts. of Computer Science, Physics & Astronomy, Chemical Engineering & Materials Science, and Biological

More information

Understanding Molecular Simulation 2009 Monte Carlo and Molecular Dynamics in different ensembles. Srikanth Sastry

Understanding Molecular Simulation 2009 Monte Carlo and Molecular Dynamics in different ensembles. Srikanth Sastry JNCASR August 20, 21 2009 Understanding Molecular Simulation 2009 Monte Carlo and Molecular Dynamics in different ensembles Srikanth Sastry Jawaharlal Nehru Centre for Advanced Scientific Research, Bangalore

More information

Temperature and Pressure Controls

Temperature and Pressure Controls Ensembles Temperature and Pressure Controls 1. (E, V, N) microcanonical (constant energy) 2. (T, V, N) canonical, constant volume 3. (T, P N) constant pressure 4. (T, V, µ) grand canonical #2, 3 or 4 are

More information

Fast Eigenvalue Solutions

Fast Eigenvalue Solutions Fast Eigenvalue Solutions Techniques! Steepest Descent/Conjugate Gradient! Davidson/Lanczos! Carr-Parrinello PDF Files will be available Where HF/DFT calculations spend time Guess ρ Form H Diagonalize

More information

Langevin Dynamics in Constant Pressure Extended Systems

Langevin Dynamics in Constant Pressure Extended Systems Langevin Dynamics in Constant Pressure Extended Systems D. Quigley and M.I.J. Probert CMMP 2004 1 Talk Outline Molecular dynamics and ensembles. Existing methods for sampling at NPT. Langevin dynamics

More information

I. BASICS OF STATISTICAL MECHANICS AND QUANTUM MECHANICS

I. BASICS OF STATISTICAL MECHANICS AND QUANTUM MECHANICS I. BASICS OF STATISTICAL MECHANICS AND QUANTUM MECHANICS Markus Holzmann LPMMC, Maison de Magistère, Grenoble, and LPTMC, Jussieu, Paris markus@lptl.jussieu.fr http://www.lptl.jussieu.fr/users/markus/cours.html

More information

Introduction to molecular dynamics

Introduction to molecular dynamics 1 Introduction to molecular dynamics Yves Lansac Université François Rabelais, Tours, France Visiting MSE, GIST for the summer Molecular Simulation 2 Molecular simulation is a computational experiment.

More information

Microcanonical Ensemble

Microcanonical Ensemble Entropy for Department of Physics, Chungbuk National University October 4, 2018 Entropy for A measure for the lack of information (ignorance): s i = log P i = log 1 P i. An average ignorance: S = k B i

More information

Ab initio molecular dynamics: Propagating the density matrix with Gaussian orbitals

Ab initio molecular dynamics: Propagating the density matrix with Gaussian orbitals JOURNAL OF CHEMICAL PHYSICS VOLUME 114, NUMBER 8 JUNE 001 Ab initio molecular dynamics: Propagating the density matrix with Gaussian orbitals H. Bernhard Schlegel and John M. Millam Department of Chemistry,

More information

Constant Pressure Langevin Dynamics: Theory and Application to the Study of Phase Behaviour in Core-Softened Systems.

Constant Pressure Langevin Dynamics: Theory and Application to the Study of Phase Behaviour in Core-Softened Systems. Constant Pressure Langevin Dynamics: Theory and Application to the Study of Phase Behaviour in Core-Softened Systems. David Quigley A thesis submitted for the degree of Doctor of Philosophy University

More information

2. Thermodynamics. Introduction. Understanding Molecular Simulation

2. Thermodynamics. Introduction. Understanding Molecular Simulation 2. Thermodynamics Introduction Molecular Simulations Molecular dynamics: solve equations of motion r 1 r 2 r n Monte Carlo: importance sampling r 1 r 2 r n How do we know our simulation is correct? Molecular

More information

Before we start: Important setup of your Computer

Before we start: Important setup of your Computer Before we start: Important setup of your Computer change directory: cd /afs/ictp/public/shared/smr2475./setup-config.sh logout login again 1 st Tutorial: The Basics of DFT Lydia Nemec and Oliver T. Hofmann

More information

Simulation of molecular systems by molecular dynamics

Simulation of molecular systems by molecular dynamics Simulation of molecular systems by molecular dynamics Yohann Moreau yohann.moreau@ujf-grenoble.fr November 26, 2015 Yohann Moreau (UJF) Molecular Dynamics, Label RFCT 2015 November 26, 2015 1 / 35 Introduction

More information

Computational Physics

Computational Physics Computational Physics Molecular Dynamics Simulations E. Carlon, M. Laleman and S. Nomidis Academic year 015/016 Contents 1 Introduction 3 Integration schemes 4.1 On the symplectic nature of the Velocity

More information

REVIEW. Hamilton s principle. based on FW-18. Variational statement of mechanics: (for conservative forces) action Equivalent to Newton s laws!

REVIEW. Hamilton s principle. based on FW-18. Variational statement of mechanics: (for conservative forces) action Equivalent to Newton s laws! Hamilton s principle Variational statement of mechanics: (for conservative forces) action Equivalent to Newton s laws! based on FW-18 REVIEW the particle takes the path that minimizes the integrated difference

More information

Introduction to DFTB. Marcus Elstner. July 28, 2006

Introduction to DFTB. Marcus Elstner. July 28, 2006 Introduction to DFTB Marcus Elstner July 28, 2006 I. Non-selfconsistent solution of the KS equations DFT can treat up to 100 atoms in routine applications, sometimes even more and about several ps in MD

More information

Basic tutorial to CPMD calculations

Basic tutorial to CPMD calculations Car and Parinnello Molecular Dynamics http://www.cpmd.org/ Basic tutorial to CPMD calculations Sébastien LE ROUX sebastien.leroux@ipcms.unistra.fr INSTITUT DE PHYSIQUE ET DE CHIMIE DES MATÉRIAUX DE STRASBOURG,

More information

Car-Parrinello Molecular Dynamics

Car-Parrinello Molecular Dynamics Car-Parrinello Molecular Dynamics Eric J. Bylaska HPCC Group Moral: A man dreams of a miracle and wakes up with loaves of bread Erich Maria Remarque Molecular Dynamics Loop (1) Compute Forces on atoms,

More information

N independent electrons in a volume V (assuming periodic boundary conditions) I] The system 3 V = ( ) k ( ) i k k k 1/2

N independent electrons in a volume V (assuming periodic boundary conditions) I] The system 3 V = ( ) k ( ) i k k k 1/2 Lecture #6. Understanding the properties of metals: the free electron model and the role of Pauli s exclusion principle.. Counting the states in the E model.. ermi energy, and momentum. 4. DOS 5. ermi-dirac

More information

Introduction to atomic scale simulations

Introduction to atomic scale simulations Powder Technology course Autumn semester 2017 Introduction to atomic scale simulations Peter M Derlet Condensed Matter Theory Paul Scherrer Institut peter.derlet@psi.ch Outline of lecture The big picture

More information

Introduction to density functional perturbation theory for lattice dynamics

Introduction to density functional perturbation theory for lattice dynamics Introduction to density functional perturbation theory for lattice dynamics SISSA and DEMOCRITOS Trieste (Italy) Outline 1 Lattice dynamic of a solid: phonons Description of a solid Equations of motion

More information

Statistical thermodynamics for MD and MC simulations

Statistical thermodynamics for MD and MC simulations Statistical thermodynamics for MD and MC simulations knowing 2 atoms and wishing to know 10 23 of them Marcus Elstner and Tomáš Kubař 22 June 2016 Introduction Thermodynamic properties of molecular systems

More information

Structure of Cement Phases from ab initio Modeling Crystalline C-S-HC

Structure of Cement Phases from ab initio Modeling Crystalline C-S-HC Structure of Cement Phases from ab initio Modeling Crystalline C-S-HC Sergey V. Churakov sergey.churakov@psi.ch Paul Scherrer Institute Switzerland Cement Phase Composition C-S-H H Solid Solution Model

More information

III. Kinetic Theory of Gases

III. Kinetic Theory of Gases III. Kinetic Theory of Gases III.A General Definitions Kinetic theory studies the macroscopic properties of large numbers of particles, starting from their (classical) equations of motion. Thermodynamics

More information

Crossing the barriers - simulations of activated processes

Crossing the barriers - simulations of activated processes Crossing the barriers - simulations of activated processes Mgr. Ján Hreha for 6 th Student Colloquium and School on Mathematical Physics Faculty of Mathematics, Physics and Informatics Comenius University

More information

Physics 342 Lecture 27. Spin. Lecture 27. Physics 342 Quantum Mechanics I

Physics 342 Lecture 27. Spin. Lecture 27. Physics 342 Quantum Mechanics I Physics 342 Lecture 27 Spin Lecture 27 Physics 342 Quantum Mechanics I Monday, April 5th, 2010 There is an intrinsic characteristic of point particles that has an analogue in but no direct derivation from

More information

CONTACT PROPERTIES OF HYDRATED SILICA SURFACES

CONTACT PROPERTIES OF HYDRATED SILICA SURFACES CONTACT PROPERTIES OF HYDRATED SILICA SURFACES by André Douzette Thesis for the degree of Master of Science Faculty of Mathematics and Natural Sciences University of Oslo January 2016 Abstract In this

More information

Set the initial conditions r i. Update neighborlist. r i. Get new forces F i

Set the initial conditions r i. Update neighborlist. r i. Get new forces F i Set the initial conditions r i t 0, v i t 0 Update neighborlist Get new forces F i r i Solve the equations of motion numerically over time step t : r i t n r i t n + v i t n v i t n + Perform T, P scaling

More information

Physics 5153 Classical Mechanics. Canonical Transformations-1

Physics 5153 Classical Mechanics. Canonical Transformations-1 1 Introduction Physics 5153 Classical Mechanics Canonical Transformations The choice of generalized coordinates used to describe a physical system is completely arbitrary, but the Lagrangian is invariant

More information

Molecular dynamics: Car-Parrinello method

Molecular dynamics: Car-Parrinello method Formulations Potential energy Initialization Verlet algorithm MD: Steps MD: Thermo and barostats CP: Car-Parrinello Atomic units MD and CP textbo Molecular dynamics: Car-Parrinello method Víctor Luaña

More information

Computational Chemistry - MD Simulations

Computational Chemistry - MD Simulations Computational Chemistry - MD Simulations P. Ojeda-May pedro.ojeda-may@umu.se Department of Chemistry/HPC2N, Umeå University, 901 87, Sweden. May 2, 2017 Table of contents 1 Basics on MD simulations Accelerated

More information

Lecture 16 March 29, 2010

Lecture 16 March 29, 2010 Lecture 16 March 29, 2010 We know Maxwell s equations the Lorentz force. Why more theory? Newton = = Hamiltonian = Quantum Mechanics Elegance! Beauty! Gauge Fields = Non-Abelian Gauge Theory = Stard Model

More information

Free energy calculations and the potential of mean force

Free energy calculations and the potential of mean force Free energy calculations and the potential of mean force IMA Workshop on Classical and Quantum Approaches in Molecular Modeling Mark Tuckerman Dept. of Chemistry and Courant Institute of Mathematical Science

More information

(# = %(& )(* +,(- Closed system, well-defined energy (or e.g. E± E/2): Microcanonical ensemble

(# = %(& )(* +,(- Closed system, well-defined energy (or e.g. E± E/2): Microcanonical ensemble Recall from before: Internal energy (or Entropy): &, *, - (# = %(& )(* +,(- Closed system, well-defined energy (or e.g. E± E/2): Microcanonical ensemble & = /01Ω maximized Ω: fundamental statistical quantity

More information

Introduction to Computer Simulations of Soft Matter Methodologies and Applications Boulder July, 19-20, 2012

Introduction to Computer Simulations of Soft Matter Methodologies and Applications Boulder July, 19-20, 2012 Introduction to Computer Simulations of Soft Matter Methodologies and Applications Boulder July, 19-20, 2012 K. Kremer Max Planck Institute for Polymer Research, Mainz Overview Simulations, general considerations

More information

arxiv: v1 [physics.comp-ph] 22 Oct 2015

arxiv: v1 [physics.comp-ph] 22 Oct 2015 arxiv:1510.06489v1 [physics.comp-ph] 22 Oct 2015 Adaptive local basis set for Kohn-Sham density functional theory in a discontinuous Galerkin framework II: Force, vibration, and molecular dynamics calculations

More information

A (short) practical introduction to kinetic theory and thermodynamic properties of gases through molecular dynamics

A (short) practical introduction to kinetic theory and thermodynamic properties of gases through molecular dynamics A (short) practical introduction to kinetic theory and thermodynamic properties of gases through molecular dynamics Miguel A. Caro mcaroba@gmail.com March 28, 2018 Contents 1 Preface 3 2 Review of thermodynamics

More information

Nanoscale simulation lectures Statistical Mechanics

Nanoscale simulation lectures Statistical Mechanics Nanoscale simulation lectures 2008 Lectures: Thursdays 4 to 6 PM Course contents: - Thermodynamics and statistical mechanics - Structure and scattering - Mean-field approaches - Inhomogeneous systems -

More information

summary of statistical physics

summary of statistical physics summary of statistical physics Matthias Pospiech University of Hannover, Germany Contents 1 Probability moments definitions 3 2 bases of thermodynamics 4 2.1 I. law of thermodynamics..........................

More information

2. The Schrödinger equation for one-particle problems. 5. Atoms and the periodic table of chemical elements

2. The Schrödinger equation for one-particle problems. 5. Atoms and the periodic table of chemical elements 1 Historical introduction The Schrödinger equation for one-particle problems 3 Mathematical tools for quantum chemistry 4 The postulates of quantum mechanics 5 Atoms and the periodic table of chemical

More information

Molecular dynamics simulation of Aquaporin-1. 4 nm

Molecular dynamics simulation of Aquaporin-1. 4 nm Molecular dynamics simulation of Aquaporin-1 4 nm Molecular Dynamics Simulations Schrödinger equation i~@ t (r, R) =H (r, R) Born-Oppenheimer approximation H e e(r; R) =E e (R) e(r; R) Nucleic motion described

More information

Lectures on basic plasma physics: Hamiltonian mechanics of charged particle motion

Lectures on basic plasma physics: Hamiltonian mechanics of charged particle motion Lectures on basic plasma physics: Hamiltonian mechanics of charged particle motion Department of applied physics, Aalto University March 8, 2016 Hamiltonian versus Newtonian mechanics Newtonian mechanics:

More information

Molecular dynamics simulations

Molecular dynamics simulations Chapter 8 Molecular dynamics simulations Most physical systems are collections of interacting objects. For example, a drop of water contains more than 10 21 water molecules, and a galaxy is a collection

More information

QUANTUM AND THERMAL MOTION IN MOLECULES FROM FIRST-PRINCIPLES

QUANTUM AND THERMAL MOTION IN MOLECULES FROM FIRST-PRINCIPLES QUANTUM AND THERMAL MOTION IN MOLECULES FROM FIRST-PRINCIPLES 1 Tapio T. Rantala, Department of Physics, Tampere University of Technology http://www.tut.fi/semiphys CONTENTS MOTIVATION PATH INTEGRAL APPROACH

More information

An Approximate DFT Method: The Density-Functional Tight-Binding (DFTB) Method

An Approximate DFT Method: The Density-Functional Tight-Binding (DFTB) Method Fakultät für Mathematik und Naturwissenschaften - Lehrstuhl für Physikalische Chemie I / Theoretische Chemie An Approximate DFT Method: The Density-Functional Tight-Binding (DFTB) Method Jan-Ole Joswig

More information

Excitation Dynamics in Quantum Dots. Oleg Prezhdo U. Washington, Seattle

Excitation Dynamics in Quantum Dots. Oleg Prezhdo U. Washington, Seattle Excitation Dynamics in Quantum Dots Oleg Prezhdo U. Washington, Seattle Warwick August 27, 2009 Outline Time-Domain Density Functional Theory & Nonadiabatic Molecular Dynamics Quantum backreaction, surface

More information

v(r i r j ) = h(r i )+ 1 N

v(r i r j ) = h(r i )+ 1 N Chapter 1 Hartree-Fock Theory 1.1 Formalism For N electrons in an external potential V ext (r), the many-electron Hamiltonian can be written as follows: N H = [ p i i=1 m +V ext(r i )]+ 1 N N v(r i r j

More information

Newtonian and extended Lagrangian dynamics

Newtonian and extended Lagrangian dynamics Newtonian and extended Lagrangian dynamics Gianni Cardini and Riccardo Chelli Dipartimento di Chimica Ugo Schiff Università di Firenze, Via della Lastruccia 3, 50019 Sesto Fno, Firenze giannicardini@unifiit,

More information

Non Adiabatic Transitions in a Simple Born Oppenheimer Scattering System

Non Adiabatic Transitions in a Simple Born Oppenheimer Scattering System 1 Non Adiabatic Transitions in a Simple Born Oppenheimer Scattering System George A. Hagedorn Department of Mathematics and Center for Statistical Mechanics, Mathematical Physics, and Theoretical Chemistry

More information

Modified Ehrenfest formalism: A new approach for large scale ab-initio molecular dynamics

Modified Ehrenfest formalism: A new approach for large scale ab-initio molecular dynamics Modified Ehrenfest formalism: A new approach for large scale ab-initio molecular dynamics Xavier Iago Andrade Valencia Dpto. Física de Materiales Universidad del País Vasco/Euskal Herriko Unibertsitatea

More information

MP203 Statistical and Thermal Physics. Jon-Ivar Skullerud and James Smith

MP203 Statistical and Thermal Physics. Jon-Ivar Skullerud and James Smith MP203 Statistical and Thermal Physics Jon-Ivar Skullerud and James Smith October 27, 2017 1 Contents 1 Introduction 3 1.1 Temperature and thermal equilibrium.................... 4 1.1.1 The zeroth law

More information

Second quantization: where quantization and particles come from?

Second quantization: where quantization and particles come from? 110 Phys460.nb 7 Second quantization: where quantization and particles come from? 7.1. Lagrangian mechanics and canonical quantization Q: How do we quantize a general system? 7.1.1.Lagrangian Lagrangian

More information

Conical Intersections. Spiridoula Matsika

Conical Intersections. Spiridoula Matsika Conical Intersections Spiridoula Matsika The Born-Oppenheimer approximation Energy TS Nuclear coordinate R ν The study of chemical systems is based on the separation of nuclear and electronic motion The

More information

Modeling Materials. Continuum, Atomistic and Multiscale Techniques. gg CAMBRIDGE ^0 TADMOR ELLAD B. HHHHM. University of Minnesota, USA

Modeling Materials. Continuum, Atomistic and Multiscale Techniques. gg CAMBRIDGE ^0 TADMOR ELLAD B. HHHHM. University of Minnesota, USA HHHHM Modeling Materials Continuum, Atomistic and Multiscale Techniques ELLAD B. TADMOR University of Minnesota, USA RONALD E. MILLER Carleton University, Canada gg CAMBRIDGE ^0 UNIVERSITY PRESS Preface

More information

Optimization with Surrogates for Electronic-Structure Calculations. Yonas Beyene Abraham

Optimization with Surrogates for Electronic-Structure Calculations. Yonas Beyene Abraham Optimization with Surrogates for Electronic-Structure Calculations by Yonas Beyene Abraham A Thesis Submitted to the Graduate Faculty of WAKE FOREST UNIVERSITY in Partial Fulfillment of the Requirements

More information

Hierarchical Multiscale Modeling of Materials: an Application to Microporous Systems

Hierarchical Multiscale Modeling of Materials: an Application to Microporous Systems Università degli Studi di Sassari Dipartimento di Chimica e Farmacia Scuola di Dottorato in Scienze e Tecnologie Chimiche Indirizzo Scienze Chimiche Ciclo XXVI Hierarchical Multiscale Modeling of Materials:

More information

ChE 210B: Advanced Topics in Equilibrium Statistical Mechanics

ChE 210B: Advanced Topics in Equilibrium Statistical Mechanics ChE 210B: Advanced Topics in Equilibrium Statistical Mechanics Glenn Fredrickson Lecture 1 Reading: 3.1-3.5 Chandler, Chapters 1 and 2 McQuarrie This course builds on the elementary concepts of statistical

More information

CE 530 Molecular Simulation

CE 530 Molecular Simulation CE 530 Molecular Simulation Lecture Molecular Dynamics Simulation David A. Kofke Department of Chemical Engineering SUNY Buffalo kofke@eng.buffalo.edu MD of hard disks intuitive Review and Preview collision

More information

Introduction to Simulation - Lectures 17, 18. Molecular Dynamics. Nicolas Hadjiconstantinou

Introduction to Simulation - Lectures 17, 18. Molecular Dynamics. Nicolas Hadjiconstantinou Introduction to Simulation - Lectures 17, 18 Molecular Dynamics Nicolas Hadjiconstantinou Molecular Dynamics Molecular dynamics is a technique for computing the equilibrium and non-equilibrium properties

More information

Part II Statistical Physics

Part II Statistical Physics Part II Statistical Physics Theorems Based on lectures by H. S. Reall Notes taken by Dexter Chua Lent 2017 These notes are not endorsed by the lecturers, and I have modified them (often significantly)

More information

Intro to ab initio methods

Intro to ab initio methods Lecture 2 Part A Intro to ab initio methods Recommended reading: Leach, Chapters 2 & 3 for QM methods For more QM methods: Essentials of Computational Chemistry by C.J. Cramer, Wiley (2002) 1 ab initio

More information

Introduction to model potential Molecular Dynamics A3hourcourseatICTP

Introduction to model potential Molecular Dynamics A3hourcourseatICTP Introduction to model potential Molecular Dynamics A3hourcourseatICTP Alessandro Mattoni 1 1 Istituto Officina dei Materiali CNR-IOM Unità di Cagliari SLACS ICTP School on numerical methods for energy,

More information