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Jen-Chang Chen E-mail: jcc67@caece 03/08/2005 1 1 PowerPoint Presentation
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Jen-Chang Chen E-mail: jcc67@caece 03/08/2005 1 1

Jen-Chang Chen E-mail: jcc67@caece 03/08/2005 1 1

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Jen-Chang Chen E-mail: jcc67@caece 03/08/2005 1 1

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  1. DL_POLY advanced training course and summaries of the “2005 Methods in Molecular Simulation Summer School” Jen-Chang Chen E-mail: 03/08/200511 NCHC DL_POLY Training Course

  2. Acknowledgements • Thanks to CCP5 and Marie Curie Actions for financial support of the summer school. • Thanks to Dr. Bill Smith and Dr. Rong-Shan Qin for informative discussions. • I also appreciate Dr. David Willock and Prof. John Harding for their kind help. NCHC DL_POLY Training Course

  3. Outlines • DL_POLY: A quick review • Practical workshop 1- DL_POLY • Inter-atomic potentials • Long ranged forces • Optimization • Hyperdynamics • Non-equilibrium MD • Meso-scale methods • Practical workshop 2- DL_MESO NCHC DL_POLY Training Course

  4. Suggested Reading I • Understanding Molecular Simulation, D. Frenkel and B. Smit, Academic Press 2002. • The Art of Molecular Dynamics Simulation, D.C. Rapaport, Cambridge 2004. • Computer Simulation of Liquids, M.P. Allen and D.J. Tildesley, Oxford 1989. • Molecular Dynamics Simulation: Elementary Methods, J.M. Haile, Wiley 1997. • A Guide to Monte Carlo Simulations in Statistical Physics, D.P. Landau and K. Binder, Cambridge 2000. • Molecular Modelling and Simulation, Tamar Schlick. Springer Verlag 2002. NCHC DL_POLY Training Course

  5. Suggested Reading II • Classical mechanics, H. Goldstein, C. Poole and J. Safko, Addison Wesley 2002. • Theory of Simple Liquids, J.-P. Hansen and I.R. McDonald, Academic Press 1986. • Statistical Mechanics of Nonequilibrium Liquids, D. Evans and G. Morriss, Academic Press 1990. • Statistical mechanics: a survival guide, Mike Glazer and Justin Wark, Oxford 2001. • Statistical Mechanics an Introduction, David H. Trevena, Harwood Publishing Ltd 2003. • An introduction to Statistical Thermodynamics, R. Gasser and W. Richards, World Scientific 1995. • Statistical Mechanics, D. A. McQuarrie, Harper Collins 1976. • An introduction to statistical thermodynamics, Terrell L. Hill, Dover 1986. • Statistical mechanics : a concise introduction for chemists, B. Widom, Cambridge, New York 2002. NCHC DL_POLY Training Course

  6. A survival guide of DL_POLY I • Please read the user manual and review the DL_POLY website at first. • • Second, read “A DL_POLY tutorial” and “FAQ.” • Finally, join the DL_POLY forum. • • A Good program library for MD, MC, LS and LD is in the following URL. • • If you like to receive the information regarding the activities of CCP5, check the following link. • NCHC DL_POLY Training Course

  7. A survival guide of DL_POLY II • CONFIG: Fixed format (Mandatory) • CONTROL: Free format (Mandatory) • FIELD: Fixed format (Mandatory) • TABLE: Fixed format (An alternative way to specify short-ranged potentials) • For a restart job: • REVCON: Rename this file to the CONFIG file (Formatted) • REVIVE: Rename or copy it to the REVOLD file (Unformatted) • REVOLD: Unformatted file • Utilize the DL_POLY Java GUI. • Input files The DL_POLY_2 User Manual, Version 2.14, P.107, 2003 NCHC DL_POLY Training Course

  8. A survival guide of DL_POLY II (cont.) • Output files REVCON: The restart configuration OUTPUT: Simulation results HISTORY: The history of atom position, velocity and force STATIS: The history of statistical data RDFDAT: The RDF data ZDNDAT: The Z density profile REVIVE: The accumulated statistical data (unformatted file) The DL_POLY_2 User Manual, Version 2.14, P.107, 2003 NCHC DL_POLY Training Course

  9. A survival guide of DL_POLY III • DL_POLY Java GUI provides “analysis” module! • You can write a program to do pre-processing or post-processing easily. • For example, I wrote few python scripts to produce the CONFIG file from GULP program, to extract statistical data and to generate a multi-frame XYZ file. • For visualization, VMD is one of good tools. • • You need a program to parse the HISTORY file to a VMD input file. • NCHC DL_POLY Training Course

  10. Example: XYZ file format 2 ## Total number of atom in this frame Step 1 ## Comment Ar 0.0 0.0 0.0 ## Symbol X Y Z Ar 0.5 0.0 0.0 ## Symbol X Y Z 2 ## Total number of atom in the SECOND frame Step 2 Ar 0.1 0.1 0.1 Ar 0.5 0.5 0.5 NCHC DL_POLY Training Course

  11. Example: #!/usr/bin/env python ## Usage: ## I. chmod 755 ## II. ./ HISTORY import sys,string atomList=['Si','Al','O_','O','H','C','Cl','Na','Zr','Y_','Mg','K','Ag'] inputFile=open(sys.argv[1],'r') outFile=open(sys.argv[2],'w') title=inputFile.readline() line=inputFile.readline() while(line!=""): if string.split(line)[0]=='timestep': timestep='step= '+string.split(line)[1]+'\n' totalAtom=string.split(line)[2]+'\n' outFile.write(totalAtom) outFile.write(timestep) if string.split(line)[0][:2] in atomList: atomName=string.split(line)[0][:2]+'\t' outFile.write(atomName) xyzline=inputFile.readline() outFile.write(xyzline) line=inputFile.readline() inputFile.close() outFile.close() NCHC DL_POLY Training Course

  12. Practical workshop 1- DL_POLY • Subject: Methane in silicalite • Objectives: • Ensure that you are familiar with DL_POLY. • Calculate diffusion coefficient by using Einstein equation. • Apply VMD or your favorite visualization tool to see the process. NCHC DL_POLY Training Course

  13. Practical workshop 1- DL_POLY (cont.) • Step 1: Download “nchc.tar.gz” into your cygwin folder. • Step 2: Do “tar zxvf nchc.tar.gz” • Step 3: Copy all the files into “execute” folder. • Step 4: Run DL_POLY Java GUI to see the CONFIG file. • Step 5: Use DL_POLY Java GUI or your favorite editor to see the FIELD and CONTROL file. • Setp 6: Run “DL_POLY.X” • Step 6: Check all output files. • Step 7: Run DL_POLY Java GUI to obtain the MSD file of “C_1” and “H_1”. • Step 8: Use your favorite visualization tool to see the diffusion process. • ./ HISTORY NCHC DL_POLY Training Course

  14. Practical workshop 1- DL_POLY (cont.) • Step 9: Utilize EXCEL to open the MSD0.XY and MSD1.XY and draw the MSD vs. Time plots. Please note the slope of the plot. • Einstein equation: NCHC DL_POLY Training Course

  15. Inter-atomic potentials • Potentials are algorithms to generate energy surfaces. • The potential terms should reflect the physics of the system and often contain a lot of approximations. • In the same species, different problems might apply different potentials. • In molecular simulation, you should have a strong feeling about potentials. • Wrong potentials == Wrong physics (GIGO) NCHC DL_POLY Training Course

  16. Basic criteria for selecting potentials • Physical meanings • Computing Efficiency (computing cost) • Think about a MD algorithm. • Accuracy • Transferability • are the potential models cheap, accurate and common in the same species, systems, phases or configurations? NCHC DL_POLY Training Course

  17. Where do you find the potentials? • Literatures • Ionic solids: • G. V. Lewis and C. R. A. Catlow, “Potential Models for Ionic Oxides,” J. Phys. C: Solid State Phys.,18(1985), 1149-1161. • C. R. A. Catlow and A. M. Stoneham, “Inoicity in Solids,” J. Phys. C: Solid State Phys.,16(1983), 4321-4338. • Chap. 8~Chap. 11, Lecture Notes in Physics Vol. 166: Computer Simulation of Solids, edited by C.R.A. Catlow and W.C.Mackrodt, Springer-Verlag,1982. • Metals • Embedded Atom Model • Sutton-Chen Model, A. P. Sutton and J. Chen, Phil. Mag. Lett., 61(1990), 139. • Polymer • • Fitting from ab-initio calculations of structure • Fitting from experimental data • Direct calculation of each term in the potential NCHC DL_POLY Training Course

  18. Before you start fitting • What are you going to fit to? • Experiment: • How good is the data you are fitting to? • Is bulk crystal data good enough to describe your situation? • What data are you using to validate the results? • Calculation: • Were the calculation done properly? • Does the method used include the effects you are fitting to? • How comprehensive is the set of configurations you have? • What functional form are you going to use? Note: This slide copied from the page 5 of Prof. John Harding’s handout. NCHC DL_POLY Training Course

  19. When you start fitting • Fitting is an optimization problem – Find local minima. • Make sure that the fitting process cannot get into unphysical regions of parameter space. • Global fitting processes do not ensure that the right physics goes to the right part of the model. • Remember that the potential cutoff is part of the model. Please quote it in your work. Note: This slide copied from the page 15 of Prof. John Harding’s handout with modifications. NCHC DL_POLY Training Course

  20. How do we fit the potentials? • GULP, General Utility Lattice Program, provides a fitting function! • • We can determine the potentials parameters by fitting to data from ab-initio calculation, normally by attempting to reproduce energy surface. • We can derive empirical potentials by trying to reproduce experimental data. NCHC DL_POLY Training Course

  21. Ab-initio calculations for fitting data • Calculate the energies of a number of different configurations relevant to the problem needed. • Fit potential using energies and structures • Calculate a dynamics trajectory. • Fit the forces and also properties to those calculated bt yhe potential form chosen. • You should know about…. • The ab-initio calculation doesn’t mean absolutely accurate. Note: This slide copied from the page 14 of Prof. John Harding’s handout with modifications. NCHC DL_POLY Training Course

  22. Long ranged forces • Long ranged forces arise from electrostatics and hydrodynamics. • For electrostatics: • The direct sum is inefficient. • Ewald summation is excellent. • Reaction field has a good physical basis. • Fast multipole method is quite slow. NCHC DL_POLY Training Course

  23. Ewald sum I • U=Reciprocal space term + Real space term – Self-interaction term NCHC DL_POLY Training Course

  24. + + + + C - - - - C+G = - + + + -G - - Ewald sum II NCHC DL_POLY Training Course

  25. Ewald sum III • The screened charge (C+G) terms are summed directly and represent short ranged interactions in real space. • Poisson’e equation is used to solve the purely gaussian terms and this provides a short ranged solution in reciprocal space. NCHC DL_POLY Training Course

  26. The adaptations of Ewald sum • Ewald-Kornfeld method deals with dipolar molecules • Smoothed particle mesh Ewald is a particle mesh method using FFT to handle reciprocal space. • Hautman-Klein method for surfaces and interfaces. NCHC DL_POLY Training Course

  27. Smoothed Particle Mesh Ewald I • SPME is the conversion of the reciprocal space component of the Ewald sum into a FFT form. NCHC DL_POLY Training Course

  28. Smoothed Particle Mesh Ewald II • SPME is generally faster than conventional Ewald sum in most applications. Algorithm scales as O(NlogN) • In some case, the 3D array can be large. • For efficiency, always use a proper FFT package. NCHC DL_POLY Training Course

  29. Optimization • Optimization is the methodology to find the minimum energy and/or to search the saddle points. • A saddle point is a minimum in all conjugate directions except one where it is a maximum. • Minimization methods: • Golden section method, direction set method, simplex method, conjugate gradient methods (steepest descent method), Newton-Raphson (variable metric) method, Levenberg-Marquandt method, genetic algorithms and simulated annealing method. NCHC DL_POLY Training Course

  30. How to select a minimization method • The selection criteria depend on the following issue. • Derivative data • Memory storage • Noisy NCHC DL_POLY Training Course

  31. Minimization method NCHC DL_POLY Training Course

  32. Methods for finding the saddle point • This part is very important for hyper-dynamics. • Nudged elastic band method • The NEB method is to find the location of the two basins separated by the saddle points. • Dimer methods and Mode-following methods • If we only know the starting basin, we can use dimer method or mode-following method to find all the saddle points that lead out of the basin. NCHC DL_POLY Training Course

  33. Hyperdynamics • References: • Dr. Arthur F. Voter • A.F. Voter, F. Montalenti and T.C. Germann, "Extending the Time Scale in Atomistic Simulation of Materials," Annu. Rev. Mater. Res., 32 (2002), 321-346. • Hyperdynamics is a method to deal with the simulation of processes that consist of rare events. The major concern is to know the long timescale (ms- μs) behaviors rather than short timescale (ps). NCHC DL_POLY Training Course

  34. Rare-event and Transition state theory • Rare event or infrequent event system • The system spends the most of time exploring a single energy basin. At some point in time, transitions to neighboring basins are very rapid. • Transition state theory • It assumes a saddle hyper-surface, once it has crossed it, cannot return. NCHC DL_POLY Training Course

  35. Hyperdynamics methods • Temperature Accelerated Dynamics • Uses a high-temperature simulation to construct a faithful representation of a low temperature trajectory by extrapolating the processes of high-temperature back to low temperature and assuming that the escape probabilities from a basin are govern by first order kinetics. • Parallel replica • Replicate a basin into many processors. Then run all systems until one shows a transition. Stop all processors and replicate new state to all processors and continue. • Bias potential hyperdynamics • Add a bias potential. Provided this goes to zero at the saddle-plane, the dynamics of the biased system has the same relative escape probabilities as the real system, but runs faster. NCHC DL_POLY Training Course

  36. Non-equilibrium MD • References: • D. J. Evans and G. P. Morriss, “Non-Newtonian Molecular dynamics,” Computer Phys. Rep., 1(1984), 297-343. • D. J. Evans and G. P. Morriss, Statistical Mechanics of Nonequilbrium Liquids, Academic Press, 1990. • NCHC DL_POLY Training Course

  37. NEMD • Enable an understanding of irreversible processes. • Calculation of transport properties. • Field dependent transport properties • Two main approaches • Direct NEMD • Replicate the non-equilibrium process exactly as it occurs in nature. • It’s not useful for obtaining bulk transport coefficients because fluid is not homogeneous, and density and temperature gradients are often present making interpretation difficult. • Synthetic Field NEMD • Invent a fictitious field that drives the system away from equilibrium. • Linear response theory can then be used to obtain the transport coefficient. NCHC DL_POLY Training Course

  38. Meso-scale methods I • References: • D. M. Heyes, J.B. Baxter, U. Tüzün and R. S. Qin, “ Discrete-element method simulation: from micro to macro scales,” Philosophical Transactions of the Royal Society London A, 362(2004),1853-1865. • S. Succi, The Lattice Boltzmann Equation, Oxford Science Publications, 2001. • P. Espanol,P. Warren, “Statistical-mechanics of dissipative particle dynamics,” Europhys. Lett., Vol. 30(1995), 191-196. • J. Monaghan, “Smoothed Particle Hydrodynamics,” Annu. Rev. Astron. Astrophys., 30 (1992), 543-574. NCHC DL_POLY Training Course

  39. Meso-scale Length: 10nm ~ 10μm and Time: 10ns ~ 1ms S. O. Nielsen et al., “Coarse grain models and the computer simulation of soft materials,” J. Phys. Con. Matter,16 (2004), R481-R512. NCHC DL_POLY Training Course

  40. Meso-scale methods II • Hydrodynamics and thermodynamics are the most important roles. • There are three meso-scale methods • Lattice Boltzmann Equation • Dissipative Particle Dynamics • Smoothed Particle Hydrodynamics NCHC DL_POLY Training Course

  41. Lattice Boltzmann Equation • LBE is a course grained lattice gas automation. • Fluid properties are mapped onto a discrete lattice. • Physical state at each lattice are described by a set of particle distribution functions. • Macroscopic fluid variables are defined via moments of the distribution function. • System evolution toward its equilibrium though the relaxation of the distribution function to its equilibrium form. • CFD people see LBE as a numerical solver for the Navier-Stokes equations. NCHC DL_POLY Training Course

  42. Dissipative Particle Dynamics • N particles move in continuous space and discrete time-steps. => Newton’s equation of motion! => MD • The particles are acted upon by three two-particle forces. Each of these forces conserves momentum and angular momentum and acts as pairwise. • Three inter-particle forces • Conservative Force • This force conserves energy and is derived from a potential energy similar to that in MD and is a soft-repulsive potentials. • Dissipative Force • This force is to slow the particles and always removes energy, as measured from their centre-of-mass frame. • Random Force • This force acts between all pairs of particles and is uncorrelated between different pairs. It adds energy to the system on average. Together with the dissipative force, this acts in some sense as a thermostat for the system. NCHC DL_POLY Training Course

  43. Smoothed Particle Hydrodynamics • SPH is a particle method and it doesn’t need grids/meshes to calculate spatial derivatives. • Physical system can be represented by a small set of properties defined at a finite set of discrete points. • The interpolation procedure allows any function at one of these points to be expressed in term of its values at neighboring points through an interpolation kernel. • Advantage • Spatial gradients are calculated analytically and we don’t need to utilize finite differences method and grids. NCHC DL_POLY Training Course

  44. Applications • Lattice Boltzmann • Complex boundary system • Multi-component phase separation • Thermodynamic problem • Dissipative Particle Dynamics • Colloid processing • Polymer system: Interactions between polymer and fiber • Biological system: Interactions between membranes and lipid acid. • Smooth Particle Hydrodynamics • The large displacement and high speed issue • For example, explosion simulation NCHC DL_POLY Training Course

  45. Introduction to DL_MESO program • DL_MESO is a general purpose mesoscopic simulation package developed at Daresbury Laboratory by Dr. Rongshan Qin. • The program contains three methods: Lattice-Boltzmann Equation, Dissipative Particle Dynamics and Smoothed Particle Hydrodynamics. • Current version is DL_MESO 1.2. • DPD will include from Version 2.0 and SPH will release from Version 3.0. • It is free for academic research. • The code is written by C++ with MPI. NCHC DL_POLY Training Course

  46. How do we apply the DL_MESO • • For a academic researcher, please download the license. Sign it and then fax/mail it to Dr. Qin. Dr. Qin will send you the code. • Commercial organizations interested in acquiring the code should contact Dr. Qin. NCHC DL_POLY Training Course

  47. Practical workshop 2- DL_MESO • Thanks to Dr. Rong-Shan Qin for providing us all sort of course materials. • The binary code will expire on August 07. • In this workshop, we have six exercises. • LBE • 2D pressure • 2D shear • 3D phase separation • 3D shear • Flow over obstacle • DPD • Phase separation NCHC DL_POLY Training Course

  48. Practical workshop 2- DL_MESO (cont.) • Step 1: Do “tar zxvf nchcmeso.tar.gz” • Step 2: Run “java xdlmeso” The GUI can only generate the LBE input files at this version. • Step 3: To edit a file. Run “java delmeso.” • Step 4: We need two file to run LBE: lbin.sys and “Define System” generates “lbin.sys”, and “Set Space” creates “” • Step 5: Go to the exercise folder: 2D_Pressure; 2D_Shear; 3DPhaseSep; 3DShear; FlowOverObstacle; and SIMPLE_DPD. • Step 6: Use an editor to see the input files. All the input files are well-prepared by Dr. Qin. • Step 7: To run LBE. Go into the exercise folder, and type “../slbe.exe” NCHC DL_POLY Training Course

  49. Practical workshop 2- DL_MESO (cont.) • Step 8: Run “java lbe2dplot” to plot the calculation results. • Step 9: In order to run DPD, go into the “SIMPLE_DPD” folder and type “../sdpd.exe”. • Step 10: Use VMD to open the “outpos.dat” file, and you can observe the dynamic process. NCHC DL_POLY Training Course