Introduction to model potential Molecular Dynamics A3hourcourseatICTP

Similar documents
What is Classical Molecular Dynamics?

Bioengineering 215. An Introduction to Molecular Dynamics for Biomolecules


Introduction to Molecular Dynamics

Molecular Dynamics Simulations

Scientific Computing II

Metropolis, 2D Ising model

The Hopf equation. The Hopf equation A toy model of fluid mechanics

CE 530 Molecular Simulation

Vibrational Motion. Chapter 5. P. J. Grandinetti. Sep. 13, Chem P. J. Grandinetti (Chem. 4300) Vibrational Motion Sep.

Figure 1: System of three coupled harmonic oscillators with fixed boundary conditions. 2 (u i+1 u i ) 2. i=0

G : Statistical Mechanics

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

Ideal Gas Behavior. NC State University

Introduction Statistical Thermodynamics. Monday, January 6, 14

Molecular Dynamics Simulation of a Nanoconfined Water Film

The Potential Energy Surface (PES) And the Basic Force Field Chem 4021/8021 Video II.iii

Hands-on : Model Potential Molecular Dynamics

Why study protein dynamics?

Department of Chemical Engineering University of California, Santa Barbara Spring Exercise 3. Due: Thursday, 5/3/12

Advanced Molecular Molecular Dynamics

A path integral approach to the Langevin equation

Imperfect Gases. NC State University

1.3 Molecular Level Presentation

Hyeyoung Shin a, Tod A. Pascal ab, William A. Goddard III abc*, and Hyungjun Kim a* Korea

Chapter 2 Experimental sources of intermolecular potentials

Plot the interatomic distances as a function of time and characterize the reactants and products through the plot. w

Lecture 3 Dynamics 29

Rate of Heating and Cooling

Using Molecular Dynamics to Compute Properties CHEM 430

4. Complex Oscillations

Mechanics IV: Oscillations

A Study of the Thermal Properties of a One. Dimensional Lennard-Jones System

The broad topic of physical metallurgy provides a basis that links the structure of materials with their properties, focusing primarily on metals.

Langevin Methods. Burkhard Dünweg Max Planck Institute for Polymer Research Ackermannweg 10 D Mainz Germany

Computer simulation methods (2) Dr. Vania Calandrini

T6.2 Molecular Mechanics

Example questions for Molecular modelling (Level 4) Dr. Adrian Mulholland

Symmetries 2 - Rotations in Space

Computer Simulation of Shock Waves in Condensed Matter. Matthew R. Farrow 2 November 2007

CDS 101/110a: Lecture 2.1 Dynamic Behavior

Theoretical physics. Deterministic chaos in classical physics. Martin Scholtz

Introduction to molecular dynamics

Physics 9 Spring 2011 Midterm 1 Solutions

Molecular Dynamics. A very brief introduction

Project 5: Molecular Dynamics

Fourier transforms, Generalised functions and Greens functions

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

Why Proteins Fold? (Parts of this presentation are based on work of Ashok Kolaskar) CS490B: Introduction to Bioinformatics Mar.

Biomolecular modeling I

Linear and Nonlinear Oscillators (Lecture 2)

1 Simple Harmonic Oscillator

From Atoms to Materials: Predictive Theory and Simulations

Physics 115/242 The leapfrog method and other symplectic algorithms for integrating Newton s laws of motion

Review: control, feedback, etc. Today s topic: state-space models of systems; linearization

An Extended van der Waals Equation of State Based on Molecular Dynamics Simulation

CO 2 molecule. Morse Potential One of the potentials used to simulate chemical bond is a Morse potential of the following form: O C O

in order to insure that the Liouville equation for f(?; t) is still valid. These equations of motion will give rise to a distribution function f(?; t)

Math 240: Spring/Mass Systems II

Energy and Forces in DFT

Molecular mechanics. classical description of molecules. Marcus Elstner and Tomáš Kubař. April 29, 2016

Nonlinear Single-Particle Dynamics in High Energy Accelerators

CDS 101/110a: Lecture 2.1 Dynamic Behavior

Lecture 11: Potential Energy Functions

Linear Response and Onsager Reciprocal Relations

Charge equilibration

Model for vibrational motion of a diatomic molecule. To solve the Schrödinger Eq. for molecules, make the Born- Oppenheimer Approximation:

Damped harmonic motion

Molecular Dynamics. What to choose in an integrator The Verlet algorithm Boundary Conditions in Space and time Reading Assignment: F&S Chapter 4

The Kolmogorov Law of turbulence

Ab initio molecular dynamics and nuclear quantum effects

MOLECULAR DYNAMIC SIMULATION OF WATER VAPOR INTERACTION WITH VARIOUS TYPES OF PORES USING HYBRID COMPUTING STRUCTURES

Molecular Dynamic Simulations in JavaScript

PH 548 Atomistic Simulation Techniques

Chemistry 432 Problem Set 1 Spring 2018 Solutions

Estimating Accuracy in Classical Molecular Simulation

Solutions 2: Simple Harmonic Oscillator and General Oscillations

Multiphase Flow and Heat Transfer

Reactive potentials and applications

L = 1 2 a(q) q2 V (q).

Math 240: Spring-mass Systems

ON THE INTEGRATION OF EQUATIONS OF MOTION: FEM AND MOLECULAR DYNAMICS PROBLEMS

The kinetic equation (Lecture 11)

Pair Potentials and Force Calculations

Scientific Computing II

Computation of Shear Viscosity: A Systems Approach

5.1 Classical Harmonic Oscillator

Review Materials Midterm 1

Interatomic Potentials. The electronic-structure problem

Modifications of the Robert- Bonamy Formalism and Further Refinement Challenges

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

Molecular Dynamics Simulation Study of Transport Properties of Diatomic Gases

Structural Bioinformatics (C3210) Molecular Mechanics

Machine learning the Born-Oppenheimer potential energy surface: from molecules to materials. Gábor Csányi Engineering Laboratory

The Two -Body Central Force Problem

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

Ordinary differential equations. Phys 420/580 Lecture 8

ε tran ε tran = nrt = 2 3 N ε tran = 2 3 nn A ε tran nn A nr ε tran = 2 N A i.e. T = R ε tran = 2

Scientific Computing II

Molecular Dynamics Simulations. Dr. Noelia Faginas Lago Dipartimento di Chimica,Biologia e Biotecnologie Università di Perugia

Transcription:

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, 2012

Outline Model potential molecular dynamics... as simple as possible 1 how to calculate forces... 2 how to numerically calculate trajectories... 3 from time evolution to thermodynamics (optimizations and equilibrium calculations)

Definitions (I) The dynamics is the study of the time evolution of a system x(t) as a result of the forces acting on it. Molecular Dynamics Molecular dynamics (MD) is the computer simulation of the physical movements of molecules and atoms of a system under study.

Definitions II The molecular constituents are treated classically when the DeBroglie length λ is smaller than the interatomic distances ( i.e. 2mk B T / >> (N/V ) 1 3,whereT is the temperature N/V the density). For classical particles the Newton equations of motion can be used to calculate the molecular trajectories x(t): mẍ(t) =F (x)

Definitions (II) Molecular: refers to atomic constituents: x(t) is the molecular or atomic trajectory (i.e. position as a function of time) potential: refers to the fact that atomic forces F (x) are obtained by interatomic potential that are functions of the atomic positions U(x) model: refers to the fact that the interatomic potential is and effective potential calibrated on suitable set of physical properties ofthe system rather than obtained from first-principles

Definitions (II) Molecular: refers to atomic constituents: x(t) is the molecular or atomic trajectory (i.e. position as a function of time) potential: refers to the fact that atomic forces F (x) are obtained by interatomic potential that are functions of the atomic positions U(x) model: refers to the fact that the interatomic potential is and effective potential calibrated on suitable set of physical properties ofthe system rather than obtained from first-principles

Definitions (II) Molecular: refers to atomic constituents: x(t) is the molecular or atomic trajectory (i.e. position as a function of time) potential: refers to the fact that atomic forces F (x) are obtained by interatomic potential that are functions of the atomic positions U(x) model: refers to the fact that the interatomic potential is and effective potential calibrated on suitable set of physical properties ofthe system rather than obtained from first-principles

Potential For an isolated (non dissipative) system the interatomic forces only depends on the particle position F (x) and are conservative (i.e. the work do not depend on the path) so derivable from an interatomic potential U(x) F = U(x) First-principles molecular dynamics: Forcesexplicitlytaking into account electrons (for example, within DFT methods) (Newton s equation are still used fot the motion of the molecular constituents) Model potential molecular dynamics: Forcesarederived F = U(x) by an analytic potential U(x; α) depending on empirical parameters α that are calibrated on experiments or on first-principles calculations.

Interatomic potential: General requirements What requirements should be satisfied by an interatomic potential (internal forces): not explicitly dependent on time U(x 1, x 2,...) translational invariant (only depending on pair vectors) U(x 1 x 2, x 1 x 3,...,x i x j ) rotational invariant (only dependent on scalar products and tensors) U(x ij )=F ( x ij x ij, x ij x kl, x ij x kl x pq ) sum of two-body, N-body term U = U(0)+ U x ij (0)x ij + 1 2 U = U(0)+ U(x ij, x jk )... 2 U(0) x ij x kl x ij x jk

Interatomic potential: General requirements sum of two-body, N-body term U = 1 2 2 U(0) x ij x kl x ij x jk +... U = U 2 (x 2 ij )+ U 3 (x ij, x ik )+...

Lennard-Jones potential Two-body potential 12-6 U( x 1,..., xn )= ij U LJ 2 (r ij ) where [( ) p ( ) q ] σ σ U2 LJ (r ij) =4ɛ r ij r ij p = 12 and q = 6 in the 12-6 Lennard-Jones potential. The minimum energy is found at rij 0 = σ(2 1 6 )=1.122σ; ɛ is a measure of the cohesive energy since U(r 0 )= ɛ; The lengths σ is such that U2 LJ (σ) =0 Forces are calculated by deriving the potential.

Lennard-Jones We define x ij = x i x j,andthemodulusr ij = x i x j so that the forces can be calculated: where ρ = r ij /σ Furthermore, U(r ij ) r ij F i = du = U(r ij) dr ij d x i r ij d x i [ = 4ɛ 12 ( ) p σ + 6 ( ) q ] σ [ = 4ɛ r ij x i r ij r ij p ρ p+1 + = x ij r ij r ij q ] 1 ρ q+1 σ = x i x j r ij r ij

Lennard-Jones forces Once the derivation of the single two-body component has been performed that it is possible to consider the overall potential: U(r ij, r ik )= i<j So when deriving it is obtained that d U = d x k i<j du(r ij ) d x k = i<j U(r ij ) [ ] du(r ij ) du(r ij ) δ ik + δ jk d x k d x k = i<j [ ] du(r ij ) du(r ij ) δ ik δ jk d x i d x i So the general idea is that we loop over the atoms in orde to construct the potential and we distribute the forces

Fortran 95 language is the Fortran 95 (http://en.wikipedia.org/wiki/fortran_95_language_features tested by gfortran compiler (version GCC 4.6.0) (GCC is the GNU compiler collection) For fortran beginners learn by trying program first.f90 Modules are also briefly reviewed Asimplemakefileis described Figure: Makefile

Exercise on force model Exercise 1: Use the codo.0 to calculate the forces for a dimer of Pt atoms step0 Figure: Parameters taken from P. M. Agrawal, Surface Science 515 (2002) 21 35

Time evolution (trajectories) Analytical trajectory for one particle 1D problem. t = ˆ x(t) 0 dx 2m(E 4ɛ [ ( σ x ) 12 ( σ x ) 6 ] ) The simplest approach is to discretize the first and the secondderivative ẋ(t) = x(t+dt) x(t) dt ẍ(t) = x(t+dt) x(t) dt x(t) x(t dt) dt dt = x(t+dt) 2x(t)+x(t dt) dt 2 and to impose the Newton equation law on acceleration. mẍ = F (x)

Time evolution (II) It is easily obtained an expressionthat make possible to evolve positions from the knowledge of accelerations x(t + dt) =2x(t) x(t dt)+dt 2 F (x(t))/m The trajectories can be calculated (error dt 4 )withouttheneedofvelocities; these latter are calculated apart v(t) = x(t+dt) x(t dt) dt The overall algorithm was adopted by Verlet(1967).

Time evolution (II) By using Taylor expansion, and relation a = F /m x(t + dt) =x(t)+dt v(t)+dt 2 F (x(t))/m v(t) =v(t)+dt F (x(t))/m)

Time evolution (III) Integrate equations of motion Velocity-Verlet algorithm VV algorithm is probably the most attractive proposed to date because of its numerically stability, convenience and simplicity: x(t + dt) =x(t)+v(t)dt + 1 2 dt2 F (x(t))/m v(t + dt) =v(t)+ 1 2dt [F (x(t))/m + F (x(t + dt)/m)]

Time evolution (IV) Integrate equations of motion Velocity-Verlet algorithm VV algorithm is probably the most attractive proposed to date because of its numerically stability, convenience and simplicity: v(t + dt 2 )=v(t)+ 1 2 dt F (x(t))/m x(t + dt) =x(t)+dt v(t + dt 2 ) v(t + dt) =v(t + dt 2 )+ 1 2dt F (x(t + dt)/m)

First order formulation The Newton equation of motion ẍ(t) =F (x) is equivalent to: { ẋ = p/m ṗ = F (x) By naming Γ(t) =(x, p) the point of the phase space representing the system, its time evolution can be written as where Γ =ilγ il =ẋ x +ṗ p The solution is formally obtained by applying the exponential operator e iγt to the point at initial time Γ(t) =e ilt Γ(0)

Time propagation The Trotter-Suzuki decomposition makes possible to approximate the exponential operator: e A+B = lim n (e A n e B e A n ) n e A 2 e B e A 2 It can be applied to the Liouville operator e ildt = e dt ẋ x +dt ṗ p = e dtẋ x +dtf (x) p e dt 2 F (x) p e dt ẋ x e dt 2 F (x) p The action of the exponential of the derivative can be calculated e α x f (x, p) =f (x + α, p) and the Velocity Verlet in the simpletic form is obtained; forintegrating thermostat and barostat a further action can be usefule αx x f (x, p) = f (xe α, p)

Choice of the timestep The timestep of molecular dynamics must be smaller than the timescale of the the molecular phenomena. A simple estimate is obtained by considering that [t] = [ EM 1 L 2] where the energies of interatomic interactions are of the order of ev = 1.6 10 19 J,atomicmassesare 10 3 /N Avogadro = 1/6.02 10 26 Kg and distances Å. [t] 1.014 10 14 10 fs S.I [E ] 1eV /atom 1.602 10 19 J [M] 1a.m.u 1.66 10 27 Kg [L] 1 10 10 m [T ] 1 10 14 s S.I [E ] kcal/mol 4.18kJ/mol [M] 1a.m.u 1.66 10 27 Kg [L] 1 10 10 m [T ] 1 10 14 s

Exercise Exercise dynamics Implement the Velocity Verlet algorithm see step1-step2

Damping and optimization Dampingis a physical effect that reduces the amplitude of oscillations in an oscillatory system. During damping the system moves towards the minimum (minimizing optimizing relaxing). Within molecular dynamics there are two simple methods for damp Zero-velocity dynamics Set all the velocities to zero at each step. v = 0 always The system evolves slowly untill the forces are zero (minimum); the system goes towards the minimum but it cannot accelerate. Damped dynamics: Set the velocities to zero if moving against the the force v(α, i) =0 if F (α, i) vel(α, i) < 0 The system evolves untill the forces are zero; the system is allowed to accelerate when moving towards the minimum.

Exercise Exercise damping 3 Implement zero velocity method for dissipating kinetic energy and reach the minimum energy configuration; use the damped dynamics method

Exercise putting atoms at random positions cluster of Pt atoms obtained by damping or by dynamics Figure: initial random positions Figure: relaxed structure

Computational workload Calculate the workload as a function of the number of particles of the system T αn 2 The N 2 scaling si associated to double loops over atoms Σ i=1,num_atomsdr ij = pos(:, i) pos(:, j) Verlet list method consists in store the list of neighboring atoms instead of calculating at each step. In order to do this it is necessarytostorethe list of atoms in sphere larger than the physical cutoff R V 1.1cutoff and to reduce the num T 1 t V N 2 +( T V 1)(N V N neigh ) α T

Fluctuations Fluctuations

Short-range interactions and truncation Let us consider an homogenerous system where the density is constant everywhere ρ = dn dv.iftheparticle-particleforces(potential)gotozero faster than F r 3 (U r 2 ),thanthetotalsumonaparticlefalls to zero as r increases. In this case F ij (r) 1 r (U 3+δ ij 1 r )andthe 2+δ total force acting on a particle i is F i < ˆ R F ij dr r 2 F (r ) R δ r ij <R 0 that tends to zero when δ > 0. This is the case of Van der Waals interactions U VdW r 6 (as well as Buckingham potential, Morse and many others). This is not the case of charge-charge U r 1 and charge-dipole U r 2 Coulombic interactions. Figure: