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e-Science Technologies in the Simulation of Complex Materials

e-Science Technologies in the Simulation of Complex Materials. L. Blanshard, R. Tyer, K. Kleese. S. A. French , D. S. Coombes, C. R. A. Catlow. B. Butchart, W. Emmerich – CS H. Nowell, S. L. Price – Chem. eMaterials. Polymorphism. prediction of polymorphs –

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e-Science Technologies in the Simulation of Complex Materials

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  1. e-Science Technologies in the Simulation of Complex Materials L. Blanshard, R. Tyer, K. Kleese S. A. French, D. S. Coombes, C. R. A. Catlow B. Butchart, W. Emmerich – CS H. Nowell, S. L. Price – Chem eMaterials

  2. Polymorphism prediction of polymorphs – a drug substance may exist as two or more crystalline phases in which the molecules are packed differently. Combinatorial Computational Catalysis explore which sites are involved in catalysis – used in diverse industries including petroleum, chemical, polymers, agrochemicals, and environmental.

  3. Polymorphism prediction of polymorphs – a drug substance may exist as two or more crystalline phases in which the molecules are packed differently. Combinatorial Computational Catalysis explore which sites are involved in catalysis – used in diverse industries including petroleum, chemical, polymers, agrochemicals, and environmental.

  4. simulations take too long to run data are distributed across many sites and systems no catalogue system output in legacy text files, different for each program few tools to access, manage and transfer data workflow management is manual licensing within distributed environment e-Science Issues to Address

  5. Acid Sites in Zeolites • Determine the extra framework cation position within the zeolite framework. • Explore which proton sites are involved in catalysis and then characterise the active sites. • To produce a database with structural models and associated vibrational modes for Si/Al ratios. • Improve understanding of the role of the Si/Al ratio in zeolite chemistry.

  6. Chabazite: 1T site, 12 Si centres per unit cell, 8 membered ring channels (3.8Å * 3.8Å).

  7. The Problem Si/Al – 11 = 4 Si/Al – 5 = 160 Si/Al – 3 = 5760 Si/Al – 2 = 184,320 The number of calculations quickly becomes an issue when realistic Si/Al ratios are considered. A Si/Al ratio of 2 would require 184,320 calculations at ~100 second each. = 5120.0 hours = 213 days of cpu time. When substitution of a second Al is considered there are now 4 * (10 * 4) possible structures as symmetry has been broken. Note this is for a very simple zeolite with 36 ions per unit cell, materials of interest have 296.

  8. MC/EM A combined MC and EM approach has been developed to model zeolitic materials with low and medium Si/Al ratios. Firstly Al is inserted into a siliceous unit cell and then charge compensate with cations.

  9. Name OpSys Arch State Activity LoadAv Mem ActvtyTime vm1-8@faraday.r IRIX65 SGI Owner Idle 1.192 128 3+03:01:02 vm1-14@tyndall.r IRIX65 SGI Unclaimed Idle 0.000 507 0+00:15:09 ising2.ri.ac. LINUX INTEL Unclaimed Idle 0.200 501 [?????] vm1-16@strutt1-4 OSF1 ALPHA Owner Idle 1.113 1024 0+0:26:46 xp2.ri.ac.uk OSF1 ALPHA Owner Idle 1.113 256 49+12:26:46 xp3.ri.ac.uk OSF1 ALPHA Unclaimed Idle 0.000 256 0+00:55:00 d8.ri.ac.uk WINNT40 INTEL Unclaimed Idle 0.000 255 0+02:09:45 ATLANTIC WINNT51 INTEL Unclaimed Idle 0.008 256 0+01:02:30 BABBLE.ri.ac. WINNT51 INTEL Unclaimed Idle 0.252 512 0+00:22:57 D500.ri.ac.uk WINNT51 INTEL Owner Idle 0.533 254 0+05:26:06 PCDAVIDC.ri.a WINNT51 INTEL Unclaimed Idle 0.000 504 0+03:51:26 e-sam.ri.ac.u WINNT51 INTEL Unclaimed Idle 0.001 512 0+03:16:39 pcalexey.ri.a WINNT51 INTEL Unclaimed Idle 0.002 256 0+00:35:53 Machines Owner Claimed Unclaimed Matched Preempting ALPHA/OSF1 18 1 0 1 0 0 INTEL/LINUX 1 0 0 1 0 0 INTEL/WINNT40 1 0 0 1 0 0 INTEL/WINNT51 14 1 0 5 0 0 SGI/IRIX65 22 15 0 7 0 0 Total 56 17 0 15 0 0 RI Condor Pool We have set up and tested a Condor pool at the RI, which has 50+ heterogeneous nodes from desktop PC’s, machines controlling instruments to main servers of the DFRL.

  10. Name OpSys Arch State Activity LoadAv Mem ActvtyTime vm1-8@faraday.r IRIX65 SGI Owner Idle 1.192 128 3+03:01:02 vm1-14@tyndall.r IRIX65 SGI Unclaimed Idle 0.000 507 0+00:15:09 ising2.ri.ac. LINUX INTEL Unclaimed Idle 0.200 501 [?????] vm1-16@strutt1-4 OSF1 ALPHA Owner Idle 1.113 1024 0+0:26:46 xp2.ri.ac.uk OSF1 ALPHA Owner Idle 1.113 256 49+12:26:46 xp3.ri.ac.uk OSF1 ALPHA Unclaimed Idle 0.000 256 0+00:55:00 d8.ri.ac.uk WINNT40 INTEL Unclaimed Idle 0.000 255 0+02:09:45 ATLANTIC WINNT51 INTEL Unclaimed Idle 0.008 256 0+01:02:30 BABBLE.ri.ac. WINNT51 INTEL Unclaimed Idle 0.252 512 0+00:22:57 D500.ri.ac.uk WINNT51 INTEL Owner Idle 0.533 254 0+05:26:06 PCDAVIDC.ri.a WINNT51 INTEL Unclaimed Idle 0.000 504 0+03:51:26 e-sam.ri.ac.u WINNT51 INTEL Unclaimed Idle 0.001 512 0+03:16:39 pcalexey.ri.a WINNT51 INTEL Unclaimed Idle 0.002 256 0+00:35:53 Machines Owner Claimed Unclaimed Matched Preempting ALPHA/OSF1 18 1 0 1 0 0 INTEL/LINUX 1 0 0 1 0 0 INTEL/WINNT40 1 0 0 1 0 0 INTEL/WINNT51 14 1 0 5 0 0 SGI/IRIX65 22 15 0 7 0 0 Total 56 17 0 15 0 0 RI Condor Pool But where is PC-CRAC???

  11. Level of Optimisation 50eV

  12. Level of Optimisation 240eV

  13. MOR • Mordenite – • 1 dimensional channel system • simulation cell contains two unit cells • 296 atoms, with 96 Si centres (referred to as T sites). • Substituting 8 T sites with 8 Na cations

  14. Workflow MC_subs Gulp Files Gulp WinXP Perl script MS Excel SRB

  15. Workflow II C++ MC_subs Si-zeo structure Interatomic pots Input file Gulp Files Batch of labelled Gulp files Script auto batch sub Script for cleaning dirs Gulp WinXP Perl script f90 Subset of data in formatted file Scommands MS Excel SRB

  16. Condor Stats Extensive use of Condor pools (UCL ~950 nodes in teaching pools). ~150 cpu-years of previously unused compute resource have been utilised in this study. Close collaboration with the NERC e-minerals project has allowed access to this resource. 150,000 calculations have been performed each with varying numbers of particles per simulation box, which means a total of ~75,000,000 particles have been included in our simulations of Mordenite to date.

  17. Condor Specifics Jobs submitted in 1,000 job batches – issue of stability. Shadows – not my game but a pain when Condor Master dies due to too many jobs hitting the queue (guilty feeling as Master was not solely running pool but also being used for science by pool administrator. Maximum number of jobs in queue.

  18. Condor Specifics Handling of data and analysis becomes RDS. However, keeping the pool full of jobs is also a tedious step when jobs are short, which is the ideal for the UCL pool (re: turning off pool once a day) – drip feeding. Thought in application design is key – many on UCL pool are TOTALLY unsuitable for UCL Condor Pool.

  19. MOR • Mordenite – • 1 dimensional channel system • simulation cell contains two unit cells • 296 atoms, with 96 Si centres (referred to as T sites). • Substituting 8 T sites with 8 Na cations

  20. 100 Configurations 0 100 20eV It can be seen that there are two distinct regions, -12079eV to -12076eV and -12075eV to -12073eV, but there is no obvious correlation between total energy and cell volume.

  21. 10000 Configurations 0 10000 25eV However, when 10,000 structures are considered it is clear that the most stable structures correspond to cation placements that do not cause the cell to expand. This requires that the cations sit in the large channel.

  22. 10000 Configurations

  23. Comparison of Regions -12079.5eV -12075.04eV

  24. Properties: Total energy, cell volume, lattice parameters, T-O distances, T-O-T bond angles, cation-framework oxygen distances, coordination of user specified species etc. Analysis mysql, allows input from a text file, C/C++ program or mysql command line and GUI

  25. Workflow III MC_subs Gulp Files Gulp WinXP mysql db SRB

  26. Property Good Bad Lattice Energy (eV) < -12070 > -12068 Al-Na average distance (Å) > 3.6 < 3.4 cell volume (Å3) < 5420 > 5475 average cation – Oxygen (Å) > 2.75 < 2.65 Building an Ensemble

  27. Validation Comparison with experiment is very promising showing a large difference in the quality of the fit between ‘good’ set and ‘bad’.

  28. Monitor

  29. Drip Feeding and Interactive Steering using Relational Databases Distributed Computing Portal User Input: Structural model Si/Al, cation types, [H2O] etc. Model/Configuration Generator Jobs db Analysis(geometry, energy, fit) Steering db Improve generation / modelstrategy Analysis db User Input: Diffraction data, chemical analysis, building units, Si/Al, cation types, [H2O] etc. D. Lewis, R. Coates, S. French UCL Chem / RI

  30. CML Workflow IV Workflow service needs to be exposed to outside world as a web service SSH CML CML Since we require new WSDL interfaces for each application it is a perfect opportunity to employ a standard representation for chemical structures. XML standard in Chemistry is CML (Chemical Markup Language) CML

  31. Key Achievement We are now doingscience that was not possible before the advancements made within e-Science.

  32. FER • Ferrite – • 2 dimensional channel system • simulation cell contains 115 atoms. • substituting at 4 T sites with 4 Na cations

  33. 100 Configurations 14eV Again there are steps in Total Energy and again this time no correlation with volume for the low number of configurations. Only 75 out of 100 configurations optimise

  34. 10000 Configurations 15eV However, this time when 10,000 structures are considered there are no clear steps in the volume. The volume still increases with decreasing stability but this is due to cell expansion caused by Al to Al interactions. Only 7500 out of 10000 optimise

  35. Comparison of Regions

  36. Comparison of Regions

  37. MFI • ZSM5 – • 3 dimensional channel system • simulation cell contains 292 atoms • substituting at 4 sites with 4 Na cations

  38. 10000 Configurations 10eV There is a step in Total Energy but this time only one and from then the trend is smooth.

  39. What Next When confirmed the lowest energy positions of Al the cation is exchanged for a proton and again energy minimised. This method will allow us to construct realistic models of low and medium Si/Al zeolites. Such structures can be used for further simulations and aid the interpretation of experimental data.

  40. Solid Solutions BaTiO3

  41. Solid Solutions BaSrTiO3

  42. Solid Solutions SrTiO3

  43. upload files as part of workflow to SRB generate metadata upload extracted data from files more extensive use of CML Ongoing and Future Work

  44. Key Achievement We are now doingscience that was not possible before the advancements made within e-Science.

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