PRISM Mid-Year Review 03.03.2009

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

2. 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

3. 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

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

5. ReaxFF: Methods, Results

6. mm m nm ms ns s ps Molecular Simulation Methods • Statistical and Continuum Methods • No atomistic details • Very big systems • Long simulation times • Used to infer macroscopic features ReaxFF • 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 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

7. ReaxFFvs Classical MD

8. ReaxFF Flow Chart input geo, control, ffield initialize system, data structures, lists generate neighbors compute bonds compute bonded forces update charges (QEq) compute nonbonded forces evolve the system

9. Qeq Accuracy is Important

10. Qeq can be the bottleneck • 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.

11. Validation: Density of Water • 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/cm3: • high inertia: slow but smooth convergence • low interia: quick but rough convergence

12. Validation: Density of Hydrocarbons • Hexane (@200K) and cyclohexane (@300K) - liquid phase • ~10000 atoms randomly placed around lattice points in a cube • NVT (@200K for hexane, @300K for cyclohexane), cube is shrunk by 1A on each side after every 7500 steps  another way to measure density.

13. Performance • We have prepared water systems of different sizes: 648, 6540, 13080, 26160 atoms • Memory usage and running times under NVE: • Profiling analysis shows how much computation time each component uses up:

14. Ongoing Effort(s) • 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)