1. November 9, 2015 Atomistic Characterization of Stable and Metastable Alumina Surfaces ECSS Symposium Jun 18, 2013 Sudhakar Pamidighantam

2. Atomistic Characterization of Stable and Metastable Alumina Surfaces PI Doug Spearot and co-PI Shawn Coleman at the University of Arkansas. Grant #: DMR130007 ECSS Team - Sudhakar Pamidighantam at NCSA - Yang Wang at PSC, - Mark Vanmoer at NCSA 2

3. Metastable Alumina Because of their fine particle size, high surface area, and catalytic activity of their surfaces, the transition aluminas (especially the  form) find applications in industry as adsorbents, catalysts or catalyst carriers, coatings, and soft abrasives. The excellent stoichiometry and stability of Al 2 O 3 help to make it an important constituent of many protective oxide scales formed on the surface of high-temperature metals and alloys. J. Am. Ceram. Soc., 81 [8] 1995–2012 (1998) 3 Stable  Form

4. 4 X-Ray Crystal Lattice Molecular and Solid State Structure Characterization and Crystal Engineering Function Materials for Diverse Applications Solid State Structure Determination and Function

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6. XRD Compute In Lammps The algorithm is sufficiently generic to be applicable to both electron and x-ray diffraction conditions and is integrated into the LAMMPS molecular dynamics simulator the algorithm is capable of performing diffraction analyses either statically (single snap shot after energy minimization) or dynamically during a molecular dynamics simulation to produce time averaged diffraction patterns at finite temperature. A visualization procedure is developed to create SAD patterns and 2θ x-ray diffraction line profiles from the intensities computed using the atomistic simulation data.

7. XRD Algorithm generates a three-dimensional mesh of points filling a volume of reciprocal space constructed from the entire domain of the atomistic simulation cell. The mesh of reciprocal lattice points is built on a rectilinear grid with spacing cn |An| −1 along each reciprocal lattice axis Bn. Each reciprocal lattice axis Bn is determined from the associated vector An corresponding to the n = 1, 2, or 3 edge of the simulation cell. each reciprocal lattice point is associated with a reciprocal lattice vector K describing the deviation between the diffracted and incident wave vectors kD and kI ---- K = kD − kI = ξB1 + ηB2 + ζB3, sin (θ) = |K| ---------- ------ --------- Braggs’s Law 1/dhkl = |KB| λ 2 Structure Factors Atomic Scattering Factor (x-ray)

8. Parallel Approach to the Simulation of Diffraction Pattern X-ray virtual diffraction code (xrd) has been implemented as a LAMMPS compute, a plugin capable of computing the diffraction pattern for the structure generated by LAMMPS The parallization of xrd is atom based, and is implemented within the framework of LAMMPS Further parallization of xrd has been explored over the k-space mesh. Built a stand-alone xrd code as a test bed K-mesh loop Parallization based on a MPI implementation has shown significant performance enhancement Another parallelization level (on top of the parallelization over the atoms) and employ extra MPI (or openMP) processes to parallelize the k-mesh is planned

9. Scaling and Memory Characteristics of Lammps_DS on Gordon Before Optimization

10. Ongoing Efforts in the K-mesh Parallelization of the XRD Compute Implement MIC offload directives in xrd to allow for taking advantage of the many core co-processors on Stampede – A project under the XSEDE Student Engagement Program. Summer intern student: Paula Romero Bermudez Implement MPI group in xrd to allow for running on a large number of cores on a distributed memory system – A XSEDE Campus Champion project. Campus Champion fellow for this project: Luis A. Cueva-Parra

11. Simulation Conditions 11 Virtual selected area electron diffraction (SAED) patterns are created by examining the region in reciprocal space intersecting the Ewald sphere of radius λ−1. Simulated 200 kV electron radiation (λ = 0.0251Å) and Cu Kα x-rays (λ = 1.54178Å) are used to create SAED patterns and 2θ x-ray line profiles. Embedded-atom method (EAM) potential is used for modeling atoms and their interaction. Table. Parameters used to compute analytical approximations of the Ni atomic scattering factors for electron and x-ray diffraction as calculated via equations (5) and (6) respectively with sin θ/λ (Å−1). Electron a a1 a2 a3 a4 a5 b1 b2 b3 b4 b5 0.3860 1.1765 1.5451 2.0730 1.3814 0.2478 1.7660 6.3107 25.2204 74.3146 X-ray b a1 a2 a3 a4 b1 b2 b3 b4 c 12.8376 3.8785 7.2920 0.2565 4.4438 12.1763 2.3800 66.3421 1.0341 ------------------------------------------------------------------------------------------------------------- a Parameters fit to equation (5) by Peng et al Peng L-M, Ren G, Dudarev S L and WhelanMJ 1996 Robust parameterization of elastic and absorptive electron atomic scattering factors Acta Crystallogr. A 52 257–76. b Parameters fit to equation (6) by Fox et al Fox A G, O’KeefeMA and TabbernorMA 1989 Relativistic Hartree–Fock x-ray and electron atomic scattering factors at high angles Acta Crystallogr. A 45 786–93 Virtual diffraction analysis of Ni [0 1 0] symmetric tilt grain boundaries S P Coleman, D E Spearot and L Capolungo, Modelling Simul. Mater. Sci. Eng. 21 (2013) 055020

12. Virtual Electron Diffraction Pattern and Visualization 12 Diffraction Intensity Lorentz Polarization Factor Virtual selected area electron diffraction (SAED) patterns are created by examining the region in reciprocal space intersecting the Ewald sphere of radius λ −1. A thin hemispherical slice of the reciprocal lattice mesh lying near the surface of the Ewald sphere is isolated and viewed parallel to the zone axis. The thickness of this slice is dependent on the resolution of the reciprocal space mesh and is chosen such that between 1-5 reciprocal lattice points

13. Integration Into SEAGrid Science Gateway Deployed Lammps_DS under the community access on Gordon Registered the application in SEAGrid Applications registry Registered ECSS team as users in SEAGrid – Separate PI ships for Mark and Doug created Added Restrictions to access to Lammps_DS to ECSS project team pending public release - Demonstrating the access restriction to the PI - Ye Fan (NCSA) implemented this and additional output/log access for debugging. Created internal single resource workflow consisting of Lammps_DS execution—XRD Compute- SAED Parsing - Visit Visualization on Gordon Tested multiple production inputs for PI and Co-PI, with varying virtual shared memory upto 760 GB over 16 nodes. 13

14. SEAGrid Access to Lammps_DS 14

15. SEAGrid- Lammps_DS Results 15

16. Integrating visualization Application output.saed converted to.xyz with sed script. Using OpenMPI build of VisIt (thanks to Amit Chourasia for building) VisIt launched with –s for Python script driver. Driver checks if user uploaded a sessionfile, uses RestoreSessionWithDifferentSources() Otherwise scene rendered with default threshold, clips and camera. 16

17. SEAGrid- Lammps_DS Postprocessing 17

18. Visualization Reciprocal space of diffractions yields a point cloud in a spherical volume. Scalar threshold, clipped and color mapped. – Discovered multi-window sessionfile rendering bug (#1472), testing tiled images instead 18

19. Next Phase in The ECSS Project Deploy same Workflow under apache Airavata-Xbaya Deploy modified workflow to use stampede for computation (Lammps_DS and XRD) and pipe data for VisIT visualization on Gordon Extend the infrastructure for VisIT session less and sessioned visualizations If time permits provide interactive VisIT steering by launching local VisIT application and contacting VisIT server at Gordon 19

20. Future Directions in Visualization (Plugin to load saed directly into VisIt) Other rendering techniques – point sprites, volume rendering Computation, visualization on different resources 20

21. Future directions Other rendering techniques – point sprites, volume rendering 21