PRISM Mid-Year Review 03.03.2009 Reactive Atomistics, Molecular Dynamics.

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Presentation transcript:

PRISM Mid-Year Review Reactive Atomistics, Molecular Dynamics

Y1 Accomplishments  Serial ReaxFF code fully validated and in production use  Initial set of analysis tools implemented  Charge Equilibriation in LAMMPS (Serial)  Prototype parallel version developed  New algorithms for charge equilibriation in both LAMMPS and ReaxFF  Initial implementation of Purdue-ReaxFF into LAMMPS

Y1 Accomplishments  Code Releases ◦ Purdue ReaxFF now in use at:  Purdue  CalTech (Goddard et al.)  MIT (Buehler et al.)  PSU (van Duin et al.)  USF (Pandit et al.)  USC (Vashishta et al.)  Sandia (Thompson, Plimpton) ◦ qEq in LAMMPS released to Sandia

Y1 Accomplishments  Production use of code: ◦ Purdue: Silica/Water ◦ USF: Silica/Water ◦ MIT: Silica/? ◦ USC: Nickel/?

ReaxFF: Methods, Results

Ab-initio Methods  QM first principles  Very few approximations,highly accurate  Electrons and nuclei treated separately  Computationally very intensive Small systems (a few hundreds) Short simulation times (ps)  Excited states, chemical reactions ps ns ss ms nm mm mm ReaxFF Statistical and Continuum Methods No atomistic details Very big systems Long simulation times Used to infer macroscopic features Classical Atomistic Methods  Many approximations  Reduced accuracy  No electronic DoF  Nanosecond simulations of nano-scale sized systems  ex: Classical MD, Monte Carlo Methods, Brownian Dynamics

ReaxFFClassical MD Dynamic bondingStatic bonding Dynamic 3-body, 4 body listsStatic 3-body, 4-body lists Complex and costly force computations Simpler force computations Needs to update charges at every timestep Static charges Shorter timesteps (0.25 fs)Longer timesteps (1-10 fs)

input geo, control, ffield initialize system, data structures, lists generate neighborscompute bonds compute bonded forces update charges (QEq) compute nonbonded forces evolve the system

QEq with a high tolerance is not satisfactory but as we lower the tolerance, time spent on QEq dominates the total computation time. Take home lesson: Set QEq threshold as high as possible without much sacrifice in accuracy OR find better ways.

Water system of 6540 atoms, started with almost ideal volume. Run NPT on the water system with different thermal & virial intertia combinations for ~0.14ns. All converge to 0.93 g/cm 3 : high inertia: slow but smooth convergence low interia: quick but rough convergence

 Hexane and cyclohexane - liquid phase  ~10000 atoms randomly placed around lattice points in a cube  NVT for for cyclohexane), cube is shrunk by 1A on each side after every 7500 steps  another way to measure density.

We have prepared water systems of different sizes: 648, 6540, 13080, atoms Memory usage and running times under NVE: Profiling analysis shows how much computation time each component uses up:

 Silica-Water Interface  Strain relaxation in Si/Ge/Si nanorods  Performance comparison between classical MD, ReaxFF and ab-initio simulations  Parallelization of the code (draft version completed)  Integration into LAMMPS (planned for summer)  Improvements to QEq (number of hierarchical accelerators designed – need to be implemented) Ongoing Effort(s)