Computational Nanoscience: An Emerging Tool for Exploring and Exploiting the Nanoscale

1. Computational Nanoscience: An Emerging Tool for Exploring and Exploiting the Nanoscale Peter T. Cummings Department of Chemical Engineering, Vanderbilt University and Nanomaterials Theory Institute and Chemical Sciences DivisionOak Ridge National Laboratory Fall Creek Falls Workshop on High-End Computing in Science and Engineering Fall Creek Falls, TN October 26-28, 2003

2. Acknowledgments • Research support: • Division of Chemical Sciences, Office of Basic Energy Sciences, U.S. Department of Energy • National Science Foundation: Interfacial, Transport and Thermodynamics, NANO and Division of Materials Research • National Energy Research Supercomputing Center (NERSC) • Center for Computational Sciences, Oak Ridge National Laboratory • Collaborators: • Clare McCabe (CSM), Milan Predota (Czech Academy of Sciences), Sharon Glotzer and John Keiffer (UMich), Matt Neurock (Virginia), David Keffer (U. Tenn), Shengting Cui (U. Tenn.) • ORNL: David Dean, Predrag Krstic, Jack Wells, Xiaoguang Zhang, Hank Cochran • Yongsheng Leng (VU) • Collin Wick (DOE Comp Sci Fellow from U. Minn.), Jose Rivera (U. Tenn), T. Ionescu (VU)

3. Outline of Talk • Introduction • Theory, Modeling and Simulation in Nanoscience • ORNL Center for Nanophase Materials Science and Nanomaterials Theory Institute • Algorithmic aside • Molecular electronics • Multiscale modeling • Nanoconfined fluid rheology and structure • Rheology of nanoconfined alkanes • Classical Simulations of Carbon Nanotubes • Sliding behavior of multiwall nanotubes • Organic-inorganic nanocomposite materials • Multiscale modeling • Conclusions

4. Introduction • Nanoscale science and engineering • Ability to work at molecular level, atom by atom, to create large structures with fundamentally new properties and functions* • At least one dimension is of the order of nanometers • Functionality is critically dependent on nanoscale size • Promise of unprecedented understanding and control over basic building blocks and properties of natural and man-made objects* • National Nanotechnology Initiative • http://www.nano.gov • $710 million approved by Congress for FY 2003. • FY 2004-2006 request: ~$2B *M. Roco, FY 2002 National Nanotechnology Investment Budget Request

5. Introduction • Theory, modeling and simulation (TMS) • Expected to play key role in nanoscale science and technology • “Nanotechnology Research Directions: IWGN Workshop Report. Vision for Nanotechnology Research and Development in the Next Decade,” edited by M.C. Roco, S. Williams, P. Alivisatos, Kluwer Academic Publisher, 2000 • Also available on-line at http://www.nano.gov • Chapter 2, Investigative Tools: Theory, Modeling, and Simulation, by D. Dixon, P. T. Cummings, and K. Hess • Discusses issues and examples • McCurdy, C. W., Stechel, E., Cummings, P. T., Hendrickson, B., and Keyes, D., "Theory and Modeling in Nanoscience: Report of the May 10-11, 2002, Workshop Conducted by the Basic Energy Sciences and Advanced Scientific Computing Advisory Committees of the Office of Science, Department of Energy • Published by DOE • Also available on the web at http://www.sc.doe.gov/bes/Theory_and_Modeling_in_Nanoscience.pdf

6. Introduction • Heirarchy of methods relevant to nanoscale science and technology • Connection to macroscale

7. Introduction • TMS advances over past 15 years relevant to nanoscience • Moore’s Law • Gordon Bell Prize: 1Gflop/s in 1988 vs. 27 Tflop/s in 2002 • More than four order of magnitude increase in 14 years • Explosion in application and utility of density functional theory • Molecular dynamics on as many as billions of atoms • Revolution in Monte Carlo methods (Gibbs ensemble, continuum configurational bias, tempering, etc) • Extraordinarily fast equilibration of systems with long relaxation times • New mesoscale methods (including dissipative particle dynamics and field-theoretic polymer simulation) • Applicable to systems with long relaxation times and large spatial scales • Quantum Monte Carlo methods for nearly exact descriptions of the electronic structures of molecules • Car-Parrinello and related methods for ab initio dynamics • Reactions, complex interfaces,…

8. Why is TMS so crucial to NSE? • Interpretation of experiment • Many experiments at the nanoscale require TMS to understand what is being measured • Unlike bulk systems, large-scale manufacturability of nanoscale systems will require deep theoretical understanding • Inherent strong dependence on spatial dimensions

9. Center for Nanophase Materials SciencesOak Ridge National Laboratory Multistory Lab/Office Building Nanofabrication Research Lab Specializing in neutron science, synthesis science, and theory/modeling/simulation • Neutron Science • Opportunity to assume world leadership using unique capabilities of neutron scattering to understand nanoscale materials and processes • Synthesis Science • Science-driven synthesis will be the enabler of new generations of advanced materials • Theory/Modeling/Simulation • The Nanomaterials Theory Institute • Scientific thrusts in 10 multidisciplinary research focus areas • Access to other major ORNL facilities • Spallation Neutron Source • High-Flux Isotope Reactor • Began Sept, 2002 • $60M for building and equipment; $18M/yr ongoing SNS HFIR Center for Nanophase Materials Sciences http://www.cnms.ornl.gov

10. Examples • Nanorheology [NSF NANO] • Nanoconfined fluid rheology and structure • Sliding double-walled carbon nanotubes • Molecular electronics [ORNL LDRD; DOE Comp Nanoscience] • Structure of self-assembled monolayers • Hybrid organic-inorganic nanocomposite materials [NSF NIRT] • Polyhedral oligomeric silsequioxane (POSS) molecules • Nanoscale complexity at metal oxide surfaces [DOE NSET, NSF NIRT] • Planar interfaces and nanoparticles • Fluids in nanopores [ORNL LDRD] • Phase equilibria of water and water/carbon dioxide mixtures in single-walled carbon nanotubes • Self-Assembly of Polyelectrolyte Structures in Solution: From Atomic Interactions to Nanoscale Assemblies [DOE NSET]

11. Algorithmic Aside • Solve Newton’s equations (or variation) for positions and velocities of atoms • Solve 6N first-order non-linear differential equations numerically, where N is typically 103-106 (as high as 109) • Time step ~10-15 s • 1 ns ~ 106 time steps, 1ms ~ 109 time steps • Numerically intensive • Complexity measure = #atoms x #time-steps • Extreme values: 1012-1014 • Protein folding for 1ms : 104 x 109

12. Algorithmic Aside • Massively parallel molecular dynamics • Domain decomposition - large systems • Replicated data - long simulation times • Scales well for homogeneous systems • Difficult to program/implement • Best suited to short-ranged forces • Easily implemented • Equally effective for short- and long-ranged forces • Does not scale well for large numbers of molecules

13. Algorithmic Aside • Time-complexity bottleneck Size is easy Time is hard

14. 1E10 • 1E9 • 1E8 • relative performance • 1E7 • 1000000 • 100000 • 10000 • 1000 • 100 • computer speed • 10 • 1 • 1970 • 1975 • 1980 • 1985 • 1990 • 1995 • 2000 Algorithmic Aside • Algorithm/theory vs hardware • David Landau, U. of Georgia

15. Outline of Talk • Introduction • Theory, Modeling and Simulation in Nanoscience • ORNL Center for Nanophase Materials Science and Nanomaterials Theory Institute • Molecular electronics • Multiscale modeling • Nanoconfined fluid rheology and structure • Rheology of nanoconfined alkanes • Classical Simulations of Carbon Nanotubes • Sliding behavior of multiwall nanotubes • Organic-inorganic nanocomposite materials • Multiscale modeling • Conclusions

16. Molecular Electronics • Motivation • Lithographic fabrication of solid-state silicon-based electronic devices that obey Moore’s law is reaching fundamental and physical limitations and becoming extremely expensive • Self-assembled molecular-based electronic systems composed of many single-molecule devices may provide a way to construct future computers with ultra dense, ultra fast molecular-sized components • Possibility of fabricating single-molecule devices proposed theoretically by Ratner • Aviram and Ratner, "Molecular rectifiers," Chem. Phys. Letts., 29, 283 (1974) • Landmark experiment • Experimental measurement of conductance of 1,4-benzenedithiol between Au contacts: Reed et al., "Conductance of a Molecular Junction," Science, 278, 252-254 (1997)

17. Molecular Electronics • Experimental uncertainties • ~30 papers by young experimental star at Bell Labs found to be fraudulent • Experiments are difficult, usually not reproducible by other groups • Established results are being questioned

19. Molecular Electronics • Experiment vs. theory • 3 orders of magnitude difference Reed et al., Science, 278, 252-254 (1997) Di Ventra, et al., Phys. Rev. Letts., 84, 979-982 (2000).

20. Molecular Electronics • Closing the gap • Cui et al. experiment • Ensures bonded contact at each end of octanethiol molecule • Conductance measured in integer increments corresponding to 1-5 contacts • Single-molecule conductance inferred • Reduces difference between theory and experiment to less then one order of magnitude Cui, et al., Science, 294, 571-574 (2001) Kipps, Science, 294, 536-537 (2001)

21. Parameterization of the molecular potentials (structure calculation) SAM structure determination (molecular dynamics) Conductance and effects beyond DFT (electronic structure, correlation methods, finite bias, ...) Molecular Electronics • Multi-level approach • Ab initio calculations to characterize gold-BDT interaction • Structure of self-assembled monolayer (SAM) of BDT on Au(111) surface • Ab initio calculation of conductance using structure within SAM

22. GEMC results Condensed phase of benzenethiol molecules adsorbed on 111 Au surface (T=298K) Structure of benzenethiol SAM by GEMC: Lines connecting sulfur-occup-ied sites show same structure as STM Structure of benzenethiol SAM by GEMC: Probabil-ity density of sulfur atom around sulfur atom High-resolution STM image of ordered structure of benzenethiol SAM. From Wan et al., J. Phys. Chem. B, 104 (2000) 3563-3569

23.   S S  Molecular Dynamics • Molecular configuration and BDT dynamics behavior • Fully occupied Au(111) surface • Structure on surface • Partly influenced by details of Au-BDT interaction • Dependent on basis function • Largely determined by intermolecular excluded volume interactions • Average tilt (), azimuthal () and twist () angles quite independent of basis function Well-ordered herringbone structure in 120° direction Krstíc et al., “Computational Chemistry for Molecular Electronics,” Comp. Mat. Res., in press (2003); Leng et al., “Structure and dynamics of benzenedithiol monolayer on Au (111) surface,” J. Phys. Chem. B, in press (2003)

24. Electronic Structure Calculations • Significant advance in theory • Closed form expression for transmission with semi-infinite leads • Krstíc et al., “Generalized conductance formula for multiband tight-binding model,” PRB, 66, 205319 (2002) • Place pseudo-molecules between leads in calculation • Consistency shows correctness “molecule 1” “molecule 2”

25. Outline of Talk • Introduction • Theory, Modeling and Simulation in Nanoscience • ORNL Center for Nanophase Materials Science and Nanomaterials Theory Institute • Molecular electronics • Multiscale modeling • Nanoconfined fluid rheology and structure • Rheology of nanoconfined alkanes • Classical Simulations of Carbon Nanotubes • Sliding behavior of multiwall nanotubes • Organic-inorganic nanocomposite materials • Multiscale modeling • Conclusions

26. Rheology of Confined Dodecane • Experimentally see an increase in viscosity of the confined fluid of several orders of magnitude above the bulk value • Dodecane confined between mica sheets at ambient conditions Experimental bulk value . Hu et al., Phys. Rev.Letts., 66, 2758-2761 (1991)

27. Hard Disk Drive Lubrication http://alme1.almaden.ibm.com/sst/storage/hdi/interaction.shtml

28. Models and Methodology • Intramolecular interactions • Intermolecular interaction Bond stretching Bond bending r q Torsional potential f Lennard-Jones

29. Displacement under Constant Shear Stress • Dodecane confined between mica sheets • Six-layer gap (2.36 nm) • Shear stress 2.8 x107 N/m2 Constant applied shear stress

30. Sliding Behavior • Dodecane confined between mica sheets • Six-layer gap (2.36 nm) • Shear stress 2.8 x107 N/m2

31. Stick-Slip Behavior • Dodecane confined between mica sheets • Six-layer gap (2.36 nm) • Shear stress 2.8 x106 N/m2

32. Stick-Slip Behavior • Dodecane confined between mica sheets • Six-layer gap (2.36 nm) • Shear stress 2.8 x106 N/m2 270 ps

33. Comparison of Simulation and Experiment • Strain rate dependent viscosity for confined dodecane S. T. Cui, C. McCabe, P. T. Cummings, H. D. Cochran, J. Chem. Phys., 118 (2003) 8941-8944

34. Rheology of Confined Alkane Liquids • Klein and Kumacheva, Science269, 816 (1995); J. Chem. Phys. 108, 6996 (1998); ibid. 108, 7010 (1998) • OMCTS and cyclohexane confined between mica sheets in surface force apparatus • When film thickness is six layers or less • Dramatic increase in viscosity of six to seven orders of magnitude • Ability to sustain a non-zero yield stress (solid-like behavior) • When film thickness seven layers or more • No yield stress (fluid-like behavior)

35. Solidification Behavior • Dodecane confined between mica sheets • Liquid-like behavior for 7 layers of fluid or more (no yield stress) • Solid-like behavior (yield stress ~ 106 N/m2) at 6 or less layers of confined fluid • First simulation studies consistent with experiments of Klein • Completely consistent with corresponding statestheory for shift in freezing temperature under confinement • Radhakrishnan et al., JCP, 112, 11048 (2000) Cui et al., J. Chem. Phys., 114 (2001) 7189–7195

36. Mica Dodecane Mica Origin of Solidification Behavior Dodecane Dodecane Dodecane Dodecane P < 0.1 MPa P = 0.1 MPa rbulk Mica compress P = 0.1 MPa rconfined Dodecane Mica

37. Origin of Solidification Behavior At 0.1 MPa: rbulk-liquid = 0.75 g/cc rbulk-solid = 0.84 g/cc

38. Outline of Talk • Introduction • Theory, Modeling and Simulation in Nanoscience • ORNL Center for Nanophase Materials Science and Nanomaterials Theory Institute • Molecular electronics • Multiscale modeling • Nanoconfined fluid rheology and structure • Rheology of nanoconfined alkanes • Classical Simulations of Carbon Nanotubes • Sliding behavior of multiwall nanotubes • Organic-inorganic nanocomposite materials • Multiscale modeling • Conclusions

39. Experimental • Separating inner shells • Cumings and Zetl, Science 289, 602 (2000). Multiwalled Length 330 nm Dynamic Strength 0.43 MPa

40. Experimental Antecedents • Separating inner shells • Yu, Yakobson, and Ruoff, J. Phys. Chem B, 104, 8764 (2000). Multiwalled Length 7,000 nm Dynamic Strength 0.08 MPa

41. Theoretical predictions • Oscillatory Behavior • Zheng and Jiang, Phys. Rev. Lett. 88, 045503 (2002). <-force released freq  length-1

42. This Work • Molecular dynamics • NVT ensemble • Multiple time step algorithm (r-RESPA) • Slow Time step of 2.2 fs • T = 298.15 K, vacuum • Spherically truncated Lennard-Jones potential • Cutoff radius of 13.6 Å • Potential: DRIEDING model • Guo, Karasawa, and Goddard, Nature351, 464 (1991). • Fully flexible model composed of Lennard-Jones sites. • Used to predict packing structures in fullerenes, pure and doped carbon nanotubes Rivera, J. L., McCabe, C., and Cummings, P. T., Nano Letters, 3 (2003) 1001-1005 [Highlighted by Phillip Ballin Nature Materials, June 12, 2003]

43. This Work • Systems Studied • Double-wall carbon nanotube • Chiral conformation: (7,0) / (9,9) • Incommensurate system • Superlubricity • Interlayer distance: 0.34 nm • Outer diameter: 1.22 nm • Lengths: 12.21 - 98.24 nm

44. Damped Oscillations - 24.56 nm

45. Damped Oscillations - 24.56 nm

46. Model Predictions 24.56 nm 12.21 nm

47. Model Predictions 49.27 nm 36.92 nm

48. Outline of Talk • Introduction • Theory, Modeling and Simulation in Nanoscience • ORNL Center for Nanophase Materials Science and Nanomaterials Theory Institute • Molecular electronics • Multiscale modeling • Nanoconfined fluid rheology and structure • Rheology of nanoconfined alkanes • Classical Simulations of Carbon Nanotubes • Sliding behavior of multiwall nanotubes • Organic-inorganic nanocomposite materials • Multiscale modeling • Conclusions

49. H O Si Multiscale Modeling of Hybrid Nanostructured Materials • Polyhedral oligomeric silsesquioxanes (POSS) • Initial focus on cubic POSS as basic nano building block • (HSiO1.5)8 • Most experimental data • Extremely versatile • Functionalized in many ways • Functionalization affects solubility, diffusivity, rheology,… • Cross-linked to create network structures • “Alloyed” with polymer • Nanocomposites • Can be synthesized on large scale • Hybrid Plastics [(RSiO1.5)8]

50. Multiscale Modeling of Hybrid Nanostructured Materials • NSF NIRT project DMR-0103399