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

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

2 Overview Simulations, general considerations Monte Carlo (MC) Basics Application to Polymers (single chains, many chains systems methods Molecular Dynamics(MD) Basics Ensembles Application to Polymers Example: Melt of linear and ring polymers Simple membrane models (DPD and Lattice Boltzmann) Multiscale Techniques

dominates Mesoscopic L 10Å - 50Å T 10-8 - 10-4 sec Entropy dominates Microscopic L 1Å - 3Å T

3 Time and length scales Properties Macroscopic domains etc. Semi macroscopic L 100Å Å T 0 (1 sec) Mesoscopic L 10Å - 50Å T sec Entropy dominates Mesoscopic L 10Å - 50Å T sec Entropy dominates Microscopic L 1Å - 3Å T sec Energy dominates (Sub)atomic electronic structure chemical reactions excited states generic/universal *** chemistry specific

4 Time and length scales Ansatz: integrate equations of motion of a classical atomistic model Macroscopic timestep Semi sec macroscopic Mesoscopic domains etc. L 100Å Å L 10Å - 50Å global relaxation T 0 (1 time sec) O(1s) T sec still small system 10 6 atoms Entropy dominates Properties At least integration time steps In most cases neither useful, nor possible Use alternative options, employ universality, focus on question to be solved Mesoscopic L 10Å - 50Å T sec Entropy dominates Microscopic L 1Å - 3Å T sec Energy dominates (Sub)atomic electronic structure chemical reactions excited states generic/universal *** chemistry specific

5 Time and length scales General Advise: Properties Macroscopic domains etc. Semi macroscopic L 100Å Å T 0 (1 sec) Mesoscopic L 10Å - 50Å T sec Entropy dominates - Models should be as simple as possible, taken the question one asks into account - Use theory information as much as possible - Avoid conserved extensive quantities, when possible (causes transport issues, slow) - Try to beat natural slow dynamics for faster averaging (cannot be used to study dynamics) Mesoscopic L 10Å - 50Å T sec Entropy dominates Microscopic L 1Å - 3Å T sec Energy dominates (Sub)atomic electronic structure chemical reactions excited states generic/universal *** chemistry specific

6 Soft Matter Theory: Comprehensive Understanding of Physical and Chemical Properties Analytic Theory Time Atomistic bilayer buckles Molecular Soft Fluid ESPResSo Finite Elements, Macrosc. Theory Quantum GROMACS Length Local Chemical Properties Scaling Behavior of Nanostructures Energy Dominance Entropy Dominance of Properties

7 Simulations, general considerations Pure MD (Newton s eq., Liouville Eq.) Deterministic dynamics MD coupled to Noise (Fokker Planck Eq.) Brownian Dynamics (Smoluchowski Eq.) Force Biased MC Pure MC Stochastic dynamics

8 Simulations, general considerations Alanine-rich regions in silk proteins C α C β PEP

9 Overview Simulations, general considerations Monte Carlo (MC) Basics Application to Polymers (single chains, many chains systems methods Molecular Dynamics(MD) Basics Ensembles Application to Polymers Example: Melt of linear and ring polymers Simple membrane models (DPD and Lattice Boltzmann) Multiscale Techniques

11 Simulations, general considerations MC models/moves for Rouse Dynamics Generate new bonds inside chain: Mimics Rouse coupling to heat bath

12 Simulations, general considerations MC models/moves for Rouse Dynamics Diamond lattice data

13 Simulations, general considerations MC models/moves for Rouse Dynamics Bond fluctuation model JCP 1991

14 Simulations, general considerations MC models/moves for Rouse Dynamics early state equilibrated Bond fluctuation model

15 Simulations, general considerations MC models/moves for Rouse Dynamics - Lattice MC methods still used by many groups - Fast algorithms - Problems at high densities early - No state simple shear etc equilibrated possible - Switch to alternative methods, continuum Bond fluctuation model

16 Hybrid methods: SCF + MC (Mueller, Daoulas, de Pablo, Schmid) M. Müller, K. Daoulas et al: Coupling SCF calculations to particle based Monte Carlo

17 Overview Simulations, general considerations Monte Carlo (MC) Basics Application to Polymers (single chains, many chains systems methods Molecular Dynamics(MD) Basics Ensembles Application to Polymers Example: Melt of linear and ring polymers Simple membrane models (DPD and Lattice Boltzmann) Multiscale Techniques

18 Basic Idea: Integrate Newton s equations of motion for a collection of N classical particles Integrate equations of motion: Microcanonical, NVE Ensemble Interaction potential e.g. LJ U LL r ii = {( σ r ii ) 12 ( σ r ii ) 6 }

Basic Idea: Integrate Newton s equations of motion for a collection of N classical particles Integrate equations of motion: Velocity Verlet, symplectic

19 Basic Idea: Integrate Newton s equations of motion for a collection of N classical particles Integrate equations of motion: Velocity Verlet, symplectic Interaction potential e.g. LJ U LL r ii = {( σ r ii ) 12 ( σ r ii ) 6 } Variants of integration scheme, cf books by Frenkel and Smit, Allen and Tildesley

20 Simulations, general considerations MD of single, isolated chain in space?? Bead-Bead interaction Plus FENE spring for bonds

21 Simulations, general considerations MD of single, isolated chain in space?? Bead-Bead interaction Plus FENE spring for bonds NVE Ensemble integration: Never equilibrates, Rouse modes do not couple strongly enough Need noise term needed! Fermi-Pasta-Ulam Problem

22 FPU Problem weakly anharmonic chain in d=1, with periodic boundary conditions E/N =0.7, r=3, α=0.1 Short time Long time No equilibration! Applies also to harmonic crystal

23 FPU Problem weakly anharmonic chain in d=1, with periodic boundary conditions E/N =0.7, r=3, α=0.1 E/N =1.2, r=3, α=0.1 non ergodic ergodic E/N =1.0, r=3, α=0.1, most probably border line non ergodic - ergodic For ergodicity need (strongly) mixing modes!

24 Simulations, general considerations FPU Problem FPU Hamiltonian for α=0 integrable, phase space is a N-dimesional manifold of the general 2N-dimensional phase space for α 0 not integrable anymore, however there is NO analytical expression, of α min, for which modes properly mix and make system ergodic Fast equilibrating MD has strongly mixing modes, thus chaotic dynamics Intrinsically instable, not deterministic for longer times

25 Simulations, general considerations FPU Problem FPU Hamiltonian for α=0 integrable, phase space is a N-dimesional manifold of the general 2N-dimensional phase space for α 0 not integrable anymore, however there is NO analytical expression, of α min, for which modes properly mix and make system ergodic Fast equilibrating MD has strongly mixing modes, thus chaotic dynamics Intrinsically instable, not deterministic for longer times Need stabilization, coupling to a thermostat -> NVT, canonical ensemble

26 MD Ensembles Integrating Newton s equations => microcanonical (NVE) stability issues for long runs MD plus Thermostat => Canonical Ensemble, NVT two options: local vs global coupling possible consequences for dynamics MD plus Barostat => constant pressure Ensemble, NPE, usually with Thermostat, NPT

27 Application: Polymer melts and networks - how to generate an equilibrated polymer melt? - role of topological constraints - ring polymers vs open chains

28 Bead-spring model K.K & G.S. Grest R Density ρ = 0.85σ -3 Dense monomeric liquid Flexible chains c l R K 2 c c = 1.7 b, b b = 0.97σ = 2 ( N 1) = l K L L = ( N 1) b

29 Equilibration of initial melt Auhl et al JCP, 2003 Run a short chain melt to equilibrium by brute force and/or algorithm with global moves This is the reference system for longer chain melts! typically N = O( Ne) R 2 ( N) = l K N Create Target Function R R 2 2 ( n) ( n) = ri ri + n ( { n 2 ), nl < n, nl > 2 l l k k 2 R /n n

30 Equilibration of initial melt Create random walks with correct statistics by Monte Carlo procedure (e.g. NRRWs) R 2 ( N) = l K N * Position walks randomly in space * Move walks around by random procedure (translation, rotation, inflection) to minimize density fluctuations * replace/exchange walks randomly to reduce density fluctuations * Slowly increase excluded volume * Control target functions permanently * Eventually complement by double-bridging moves

31 Equilibration of initial melt Create random walks with correct statistics by Monte Carlo procedure (e.g. NRRWs) R 2 ( N) = l K N * Position walks randomly in space * Move walks around by random procedure (translation, rotation, inflection) to minimize density fluctuations * replace/exchange walks randomly to reduce density fluctuations

32 * Slowly increase excluded volume * Control target functions permanently * Eventually complement by double-bridging moves MD runs plus very slow insertions of the excluded volume bad good

33 Equilibration of initial melt R 2 ( n) ( = ri ri + n ) 2 R 2 ( n) 2 R /n { n 2, nl n, nl < > l l k k Stiff chains n Flexible chains

34 Equilibration of initial melt: ABSOLUTELY CRUCIAL

35 DPD: Dissipative Particle Dynamics shear viscosity diffusion

36 General Literature Reviews - Adv. Polymer Science Vol. 173 (2005), 185 (2005), 221 (2009), Springer Verlag, Berlin, New York, C. Holm, K. Kremer Eds. - S. J. Marrink et al, Biochimica Biophysica Acta, 2008, general review on lipid models and membranes - C. Peter, K. Kremer, Introductory Lecture for FD 144 Faraday Discuss., 144, 9 (2010) Books: - Frenkel and Smit, Understanding Molecular Simulations, Academic Press, Allen and Tildesley, Computer Simulatiosn of Liquids, Clarendon Press, G. Voth, ed., Coarse-Graining of Condensed Phase and Biomolecular Systems, Taylor and Francis, 2009

Basics of Statistical Mechanics

Basics of Statistical Mechanics Review of ensembles Microcanonical, canonical, Maxwell-Boltzmann Constant pressure, temperature, volume, Thermodynamic limit Ergodicity (see online notes also) Reading assignment: