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Software Project

Software Project. Brent Fultz California Institute of Technology. Software Functions Full Experiment Simulations Inversions of Dynamics Models. Discussions and Planning Meetings.

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Software Project

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  1. Software Project Brent Fultz California Institute of Technology • Software Functions • Full Experiment Simulations • Inversions of Dynamics Models

  2. Discussions and Planning Meetings • Sept. 24, 2001 meeting at Argonne with video to Oak Ridge, McMaster U., Caltech • Nov. 29, 2001 small meeting at Caltech with postdoc interview. • March 15, 2002 Software Workshop

  3. Software Roadmap v. 1.0

  4. Software Lifetime Exceeds Hardware Lifetime main () { FILE *fp; /*MEM pair 7/19/89 B. Fultz This program generates kinetic paths in a binary alloy with B2 structure. The level of approximation in the kinetic equations is equivalent to the pair (Bethe) approximation in thermodynamics. Note date above. Code runs just fine, migrated through workstations: austrl, ulysses, hyperfine1, hyperfine2, ARCS

  5. Software Engineering Reality: Cannot rewrite all codes in one language such as C++, Java or FORTRAN90 Need New Approach: • Modular software objects connected by object-oriented scripting language • Different high-level scripts for different experimenters • Span from detectors to dispersions on common software framework • Python

  6. For example, given a solver routine such as Writing Python Bindings double adlib::StableTimeStep(); and a wrapper char pyadlib_stableTimestep__name__[] = “stableTimestep”; PyObject * pyaldib_stableTimestep(PyObject *, PyObject * args) { double dt = adlib::StableTimeStep(“deformation”); return Py_BuildValue(“d”, dt); } one can place the result of the routine in a python variable dt = pyadlib.stableTimestep()

  7. Software for Data Acquisition

  8. Software for Data Acquisition Within 2 Hours Discovery! A feature in the data! Is it magnon, phonon, spurion?

  9. Software for Data Analysis Before Publication Relate Data to Dynamics Model

  10. Summary: ARCS Software • Tools Are Required During Data Acquisition • Users want software like a smart manservant • (to take initiative, but with discretion, and no pay) • ARCS needs an engineer to consult on the • “user experience” • Software for Data Analysis Enables New Science: • Full Experiment Simulations (connect to theory) • Analytical Inversions of Data • Single Crystal Data

  11. S(Q,E) from TOF Data • Bare minimum for users to take home • General – model independent • 3D single crystals, with real-time decisions on sample orientation • Absolute units [barns, meV, sr, atom, and Å-1] • Calibration • Handle resolution and background • Data visualization

  12. Working with S(Q,E) • Visualization • Comparisons of data sets, arithmetic operations • Analytical results from the theory of thermal neutron scattering by condensed matter • Inversions of measured data to obtain force constants or exchange energies. • Data mining — e.g., recognition of dispersions

  13. Full Experiment Simulations

  14. Neutron Wave (or wavepacket)

  15. EAM Simulations of Phonon DOS from Ni3Al Large molecular dynamics model. Computationally challenging. Calculated: PRB 57 (2): 862-869 JAN 1 1998

  16. Scattering Simulations ofLocal Magnetic Dynamics in a Disordered System

  17. 400 300 3 GPa 12 GPa 17 GPa Counts 200 100 0 -60 -40 -20 0 20 40 60 Energy ( meV ) Model Inversions — Incoherent Inelastic Scattering from 57Fe • Nuclear resonant scattering of x-rays by 57Fe at APS 3ID • Pd357Fe in diamond-anvil cell • Same analysis as incoherent neutron scattering • Invert data from 57Fe to obtain full lattice dynamics

  18. Result of Inversion — Phonon Partial DOS Curves

  19. Fe 0.4 Pd 0 GPa 0.3 Partial DOS 0.2 3 GPa 0.1 17 GPa 0.0 0 10 20 30 40 Energy (meV) Phonon Partial DOS Curves Under Pressure

  20. 50 Pd-Pd 40 30 Radial 1nn Force Constant (N/m) Pd-Fe 20 10 0 0 5 10 15 Pressure (GPa) Result of Inversion — Radial 1nn Force Constants vs. Pressure Note: Pd-Pd force constants from 57Fe scattering! L12 Pd3Fe

  21. Fifty initial conditions for Pd3Fe Try different numbers of nearest neighborsQuality of fit to experimental data

  22. Pd3Fe force constants from inversions 1nn Pd-Pd and Pd-Fe

  23. A case of bad 1nn Pd3Fe force constants

  24. Future -- S(Q,E) inversions using coherent scattering Inversions of incoherent inelastic scattering from 57Fe overcame the neutron-weighting problem in Fe alloys. More information is available through the Q- or Q-dependence of coherent scattering.

  25. S(Q,E) inversions using coherent scattering Polycrystalline Ni3Al measured at HFIR with 4 values of Q (We were looking at order-disorder phenomena...) Calculated incoherent scattering with Born-von Karman code. Calculated coherent scattering from all orientations of crystallites w.r.t. Q Coherent scattering was considered a nuisance to be overcome so we could produce a phonon DOS.

  26. S(Q,E) inversions using coherent scattering Polycrystalline Ni3Al measured at HFIR with 4 values of Q

  27. S(Q,E) inversions using coherent scattering Isotropic average of S(Q,E) of polycrystalline Ce, a coherent scatterer.

  28. Summary • A roadmap exists for the ARCS software project, • but it is still subject to change. • Individual roads are modules connected by Python: • S(Q,E) • visualization • fits to models • full experiment simulation • Work is underway on phonon dynamics inversions • incoherent scattering is useful • coherent scattering should be even better • The year 2002 will be an assessment of existing • packages. Please let us know your views!

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