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Situated @: Bath; Birkbeck; Cambridge; CCLRC Daresbury Reading The Royal Institution

eMinerals one of NERCs eScience testbed projects. eMinerals: Science Outcomes enabled by new Grid Tools. Maria Alfredsson Nottingham 21/9/2005. The eMinerals team: Environmental scientists; Chemists; Physicists; Computational and Grid scientists. PI: Martin Dove

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Situated @: Bath; Birkbeck; Cambridge; CCLRC Daresbury Reading The Royal Institution

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  1. eMinerals one of NERCs eScience testbed projects eMinerals: Science Outcomes enabled by new Grid Tools Maria Alfredsson Nottingham 21/9/2005 The eMinerals team: Environmental scientists; Chemists; Physicists; Computational and Grid scientists. PI: Martin Dove (martin@esc.cam.ac.uk) Web: www.eminerals.org Situated @: Bath; Birkbeck; Cambridge; CCLRC Daresbury Reading The Royal Institution University College London (UCL)

  2. eMinerals one of NERCs eScience testbed projects • Research undertaken by: • Bath group: • Marmier, D.J. Cooke, S.C. Parker • Birkbeck group: • Z. Du and N.H. de Leeuw • Cambridge group: • K. Trachenko, E. Artacho, J.M Pruneda, • M.T. Dove • Daresbury group: • I. Todorov and W. Smith • RI group: • M. Blanchard and K. Wright • UCL group: • M. Alfredsson, • J.P. Brodholt and • G.D. Price

  3. Environmental Processes eMinerals one of NERCs eScience testbed projects AIM: we use computational modelling to research mineralogical processes at an atomistic level, providing information on transport and immobilisation processes of pollutants, including both toxic elements (.i.e. As, Cd, Pb and organic molecules) as well as radioactive waste. We have also looked alternative energy resources to fossil fuels. • Sources of pollution e.g.: • Acid mine drainage • Land filling sites • Industries and farming • Accidents with toxics • Natural catastrophes or mineralogical properties

  4. Environmental Processes eMinerals one of NERCs eScience testbed projects Problem: Relastic models of mineral process are computationally very expensive. Solution: GRID COMPUTING • Layout: • Grid Resources • Data Management • Science Outcomes

  5. Grid Resources: • Lakes (Bath, Cambridge, UCL): • 4 linux-based clusters • 88 nodes in total with 2Gb memory per node • Pond (Cambridge): 1 Apple Xserve cluster • 8 nodes with 8Gb memory per node • 24-node IBM cluster (Reading) • 3Condor-pools: • UCL > 900 machines • Cambridge (25 machines) • Bath Resources marked in red suitable for first principles code green represents resources suitable for inter-atomic potential codes. NGS–CSAR-HPCx

  6. Data Management • Storage Resource Broker (SRB) • Bath, Cambridge, Reading and the central MCAT at Daresbury • Chemical Markup Language (CML) • -version of XML adapted for chemical applications • -All codes developed in eMinerals support CML • Metadata • Rcommands • MAST • Personal Interface Grid (PIG) • WIKI

  7. Job Submission: Submit jobs from all machines from our work station. • Globus (GSI/X.509-certificaes) Dagman and Perl scripts • Condor-G automatic meta-scheduler to submit to the “most appropriate” machine in the mini-grid. • Seagull Computer Codes: • Maintained and developed with eMinerals: • DL_Poly • Metadise – Monte Carlo implemented • Siesta • Casino • Other Codes: • Gulp • Marvin • AbInit • Casino • VASP • Crystal

  8. eMinerals one of NERCs eScience testbed projects • Science Outcome: • Surface and Interfaces • Determine water exchange and diffusion coefficient • Effect of impurites • Phase Transitions • due to compositional and pressure effects • Lattice dynamics calculations to determine most stable • polymorph • Radioactive waste

  9. Mineral/Solvent Interfaces Aim: To fully understand transport and immobilisation processes of contaminants we need an accurate description of the mineral/solvent interfaces. Solution: We perform Molecular Dynamics simulations using the DL_POLY code. Computer resources: Condor-pool - distributing many independent calculations over the machines available, using Dagman or Perl scripts  good statistical data, which can be used to determine diffusion and water exchange coefficients. NGS HPCx – larger jobs Snapshot of Goethite/Solvent interface using MD-simulation on the HPCx. A. Marmier, D. Cooke, S. Kerisit and S.C. Parker Bath University.

  10. Mineral/Solvent Interfaces • Result: • Ordering of the water molecules close to mineral surface. • Cl- ions order closer to the mineral surface than Na+ ions • The classical models • of the electrical double • layer do not describe • correctly the ion • distribution close to the • surface. A. Marmier, D.J. Cooke, S. Kerisit and S.C. Parker Bath University.

  11. Pt/Graphite interface • Graphite: Model for organic substrate • Pt/Graphite: Alternative (renewable) energy resource to fossil fuels know to generate green house gases. • Marmier and • S.C. Parker at University of Bath

  12. Pt/Graphite interface • Marmier and • S.C. Parker at University of Bath Aim: Derive highly quality empirical potentials from density functional theory (DFT) calcualtions. Problem: Computational costly Solution: Grid computing - NGS

  13. Pt/Graphite interface • Conclusions: • Most stable site • is located on a bridge • site • The activation • barrier is 0.5 eV • The adsorption • sites and energies • are different for • inter-atomic • potential calculations • Marmier and • S.C. Parker at University of Bath

  14. CaO-termimated Mineral Surfaces • Calculations: • investigate 10-20 surfaces • 2 to 5 surface terminations • 4 to 16 impurity positions • > 4 concentrations • Total number of calculations • per impurity: 120-2440 {001} surfaces of CaTiO3 TiO2-termimated • Computer Resources: • Condor Cluster • SRB M. Alfredsson, J.P. Brodholt and G.D. Price UCL

  15. Mineral Surfaces increasing concentration We defined a new method to calculate surface energies which allow us to determine crystal particle shape. We find particle shapes change with concentration of the impurity and the type of dopant. Important to understand the reactivity and inter- actions between pollutants and minerals.

  16. Cs Li Na Compositional Phase Transitions In all mineral processes we are dealing with impurities, which may changes the crystal structures Phyllosilicates (layered silicate minerals, including clays) are known to adsorb and store toxic elements. Here we show how the crystal structure of layered Li2Si2O5 transforms (‘breaks up’) in the presence of different elements, e.g. Cs. • Computational Resources: • Condor Pools • Eminerals mini-grid • SRB Z. Du and N. H. de Leeuw Birkbeck College and UCL

  17. Li Na Compositional Phase Transitions • Results: • Solid solutions of guest ions in silicates are often • thermodynamically stable. • Cation exchange from solution is an endothermic • process; only K-Na exchange expected to occur Z. Du and N. H. de Leeuw: Birkbeck College and UCL

  18. M. Blanchard and K. Wright at the RI Pyrite (Fools gold): FeS2 • Fe-bearing minerals active role in the control of acid mine • drainage and transport of heavy metals like As. • Transport and imobilisation process: • Pyrite may contain ca. 10wt% of As • Adsorption of As on Pyrite surface Aim: understanding electronic structure and bonding properties of pure pyrite. Possible phase transitions? Method: linear respons phonon calculations, using DFT Computational resources: HPCx linking back to the SRBs

  19. Pyrite (Fools gold): FeS2 • Results: • Pyrite is an insulator (in agreement • with experiment) • Pyrite is described by S2 molecules • interacting with Fe ions • Conclusions: • Calculated frequencies are in good agreement with experiment • All vibrational modes show non-linear pressure dependence • Mode Grüneisen parameters give information about • thermodynamical properties M. Blanchard and K. Wright at the RI

  20. *AE=All-electron **PP=Pseudo-potential HF-PP** QMC-PP** HF-AE* Expt.1) a (Å) B0 (GPa) 4.19 157 4.195 184 4.089 196 4.094 178 1) M. I. McCarthy et al PRB (1994) and ref. therein Note: The PP used in the HF and QMC calculations is the same. Pressure Induced Phase diagrams: MgO and FeO by UCL-team Problem: Traditional DFT techniques often fail in reproducing Fe-bearing minerals Solution: Quantum Monte Carlo (QMC) calculations Hybrid-DFT calculations Problem: QMC calculations are ca. 1000 times more computer intensive than traditional first principles calculations. Solution: HPCx – the CASINO code show excellent scaling

  21. Transition Pressure (PT) B1 to B2: QMC P(GPa) Method GGA-PAW GGA-PP(PW) GGA-PP(PW) LDA-LAPW LDA-PP(PW) 509 489 664 510 451 PT ~ 597GPa B2 59720 QMC-PP LDA-PP (PW) 569 B1 PP=Pseudo-potential Oganov et al JPC 2003 and ref. therein This work by UCL-team PT calculated from HB1=HB2; Birch-Murnaghan 3rd order EOS Result: QMC and LDA (with the same PP) give similar results Observeration: We consumed ca. 200.000 Cpu Hrs

  22. P~115 GPa at T=0K B8(NM) i-B8(AFM) r-B1(AFM) P(GPa) insulator insulator metallic Fei & Mao, Science (1994) 83 145 Phase Diagram and Crystal Structures by UCL-team Aim: Find alternative to QMC Solution: Hybrid-DFT • To determine phase transitions we need to: • optimise the geometries for all the possible crystal structures at various pressures.  ~ 240 calculations for FeO • for up to 10 computational methods (Hamiltonians)  ~240 x 10 = ~2400 calculations TNéel =193 K • Solution: • Condor cluster @UCL • SRB

  23. K. Trachenko, M.T. Dove I. Todorov and W. Smith Radioactive Waste Nuclear waste disposal – encapsulation in ceramic materials Aim: Find the best waste form to be used to immobilise surplus Pu and high-radiation waste (hrw) Problem: Most of the currently considered waste forms are damaged (amorphorised) by irradiation from hrw

  24. K. Trachenko, M.T. Dove I. Todorov and W. Smith Radioactive Waste Observation of amorphisation in Zircon

  25. K. Trachenko, M.T. Dove I. Todorov and W. Smith Radioactive Waste Nuclear waste disposal – encapsulation in ceramic materials Aim: Find the best waste form to be used to immobilise surplus Pu and high-radiation waste (hrw) • Problem: • Most of the currently considered waste forms are damaged • (amorphorised) by irradiation from hrw. • Amorphisation requires large • computational system sizes Code development: DL_Poly 5 million atoms using the HPCx

  26. K. Trachenko, M.T. Dove I. Todorov and W. Smith SiO2 GeO2 TiO2 Al2O3 MgO Radioactive waste Evolution of time Result: The more ionic properties the ceramics show the faster healing processes are observed. Snapshot of MD-generated structures caused by 40 keV U recoil. Increasing ionicity

  27. Level of theory Quantum Monte Carlo Linear-scaling quantum mechanics Natural organic matter Oxides/hydroxides Aluminosilicates Clays, micas Phosphates Carbonates Sulphides Large empirical models Organic molecules Adsorbing surface Metallic elements Halogens Contaminant • Prior the eMinerals: • project the data presented here would take several years, • involving many projects. • many of the calculations on realistic systems were also out • of reach, such as the modelling of the electrical double layer • at the solvent/mineral interface, and the • radiation damage, using more than • 5 millions ions in the simulation. • Future: • “team projects” • automatic work flows • for job submission and • data analysis.

  28. eMinerals one of NERCs eScience testbed projects Acknowledgement: The “Eminerals team” NERC for financial support Web: www.eminerals.org

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