Investigative Tools: Theory, Modeling, and Simulation

Chapter
Part of the Science Policy Reports book series (SCIPOLICY, volume 1)

Abstract

As subsequent chapters in this report will describe, theory, modeling, and simulation (TM&S) play a significant role in almost every branch of nanotechnology. TM&S consists of three distinct components. A theory can be defined as a set of scientific principles that explains phenomena—a succinct description of a class of problems. Modeling is the analytical/numerical applications of theory to solve specific problems. Simulation aims to faithfully render the physical problem in the greatest possible detail, so that the critical features emerge organically—not as a consequence of the ingenuity, insights, high level abstractions, and simplifications that characterize modeling. Each of the three TM&S components plays an important role, but the opportunities of the next decade will require a stronger emphasis on the modeling component. Multiscale modeling, in particular, will be essential in addressing the next decade’s challenges in technology exploration and nanomanufacturing. Finally, it should be understood that each subdiscipline of nanotechnology has its own TM&S community; these communities share many commonalities in underlying theoretical foundations, numerical and computational methods, and modeling approaches. This chapter focuses on issues, challenges, and opportunities common to TM&S across the broad spectrum of nanotechnology.

Keywords

Theory multiscale modeling Computer simulations Ab initio Density functional theory Molecular dynamics High performance computing Cyber-infrastructure Nanomaterials and nanosystems by design International perspective 

References

  1. 1.
    M.C. Roco, R.S. Williams, P. Alivisatos (eds.), Nanotechnology Research Directions: IWGN [NSTC] Workshop Report: Vision for Nanotechnology R&D in the Next Decade (International Technology Research Institute at Loyola College, Baltimore, 1999). Available online: http://www.nano.gov/html/res/pubs.html
  2. 2.
    N. Goldenfeld, L.P. Kadanoff, Simple lessons from complexity. Science 284, 87–89 (1999)CrossRefGoogle Scholar
  3. 3.
    S.Y. Quek, H.J. Choi, S.G. Louie, J.B. Neaton, Length dependence of conductance in aromatic single-molecule junctions. Nano Lett. 9, 3949 (2009)CrossRefGoogle Scholar
  4. 4.
    S.Y. Quek, M. Kamenetska, M.L. Steigerwald, H.J. Choi, S.G. Louie, M.S. Hybertsen, J.B. Neaton, L. Venkataraman, Dependence of single-molecule junction conductance on molecular conformation. Nat. Nanotechnol. 4, 230 (2009)CrossRefGoogle Scholar
  5. 5.
    S.Y. Quek, L. Venkataraman, H.J. Choi, S.G. Louie, M.S. Hybertsen, J.B. Neaton, Amine-gold linked single-molecule circuits: experiment and theory. Nano Lett. 7, 3477–3482 (2007)CrossRefGoogle Scholar
  6. 6.
    L. Venkataraman, J.E. Klare, C. Nuckrolls, M.S. Hybertsen, M.L. Steigerwald, Dependence of single-molecule junction conductance on molecular conformation. Nature 442, 904–907 (2006a)CrossRefGoogle Scholar
  7. 7.
    L. Venkataraman, J.E. Klare, C. Nuckrolls, M.S. Hybertsen, M.L. Steigerwald, Single-molecule circuits with well-defined molecular conductance. Nano Lett. 6, 458–462 (2006b)CrossRefGoogle Scholar
  8. 8.
    M. Lundstrom, Z. Ren, Essential physics of carrier transport in nanoscale MOSFETs. IEEE Trans. Electron Dev. 49, 133–141 (2002)CrossRefGoogle Scholar
  9. 9.
    Q. Cao, N. Kim, N. Pimparkar, J.P. Kulkarni, C. Wang, M. Shim, K. Roy, M.A. Alam, J. Rogers, H.-S. Kim, Medium scale carbon nanotube thin film integrated circuits on flexible plastic substrates. Nature 454, 495–500 (2008)CrossRefGoogle Scholar
  10. 10.
    Q. Cao, J. Rogers, M.A. Alam, N. Pimparkar, Theory and practice of ‘striping’ for improved on/off ratio in carbon nanotube thin film transistors. Nano Res. 2(2), 167–175 (2009)CrossRefGoogle Scholar
  11. 11.
    S. Heinze, J. Tersoff, R. Martel, V. Dercycke, J. Appenzeller, P. Avouris, Carbon nanotubes as Schottky barrier transistors. Phys. Rev. Lett. 89, 106801 (2002). doi: 10.1103/PhysRevLett.89.106801 CrossRefGoogle Scholar
  12. 12.
    A. Javey, J. Guo, Q. Wang, M. Lundstrom, H. Dai, Ballistic carbon nanotube field-effect transistors. Nature 424, 654–657 (2003)CrossRefGoogle Scholar
  13. 13.
    L. Berger, Emission of spin waves by a magnetic multilayer traversed by a current. Phys. Rev. B 54, 9353–9358 (1996)CrossRefGoogle Scholar
  14. 14.
    J.C. Slonczewski, Current-driven excitation of magnetic multilayers. J. Magn. Magn. Mater. 159, L1–L7 (1996)CrossRefGoogle Scholar
  15. 15.
    J.A. Katine, F.J. Albert, R.A. Buhrman, E.B. Myers, D.C. Ralph, Current-driven magnetization reversal and spin-wave excitations in Co/Cu/Co pillars. Phys. Rev. Lett. 84, 3149–3152 (2000). doi: 10.1103/PhysRevLett.84.3149 CrossRefGoogle Scholar
  16. 16.
    M. Tsoi, A.G.M. Jansen, J. Bass, W.-C. Chiang, M. Seck, V. Tsoi, P. Wyder, Excitation of a magnetic multilayer by an electric current. Phys. Rev. Lett. 80, 4281–4284 (1998). doi: 10.1103/PhysRevLett.80.4281 CrossRefGoogle Scholar
  17. 17.
    Y. Kamihara, T. Watanabe, M. Hirano, H. Hosono, Iron-based layered superconductor La[O1-xFx]FeAs (x  =  0.05-0.12) with TC  =  26 K. J. Am. Chem. Soc. 130, 3296–3297 (2008). doi: 10.1021/ja800073m CrossRefGoogle Scholar
  18. 18.
    T.A. Maier, D. Poilblanc, D.J. Scalapino, Dynamics of the pairing interaction in the Hubbard and t-J models of high-temperature superconductors. Phys. Rev. Lett. 100, 237001 (2008). doi: 10.1103/PhysRevLett. 100.237001 CrossRefGoogle Scholar
  19. 19.
    Z. Li, Y. Chen, X. Li, T.I. Kamins, K. Nauka, R.S. Williams, Sequence-specific label-free DNA sensors based on silicon nanowires. Nano Lett. 4, 245–247 (2004). doi: 10.1021/nl034958e CrossRefGoogle Scholar
  20. 20.
    Z. Li, B. Rajendran, T.I. Kamins, X. Li, Y. Chen, R.S. Williams, Silicon nanowires for sequence-specific DNA sensing: device fabrication and simulation. Appl. Phys. Mater. 80, 1257 (2005). doi: 10.1007/s00339-004-3157-1 CrossRefGoogle Scholar
  21. 21.
    A. Star, E. Tu, J. Niemann, J.-C.P. Gabriel, C.S. Joiner, C. Valcke, Label-free detection of DNA hybridization using carbon nanotube network field-effect transistors. Proc. Natl. Acad. Sci. U.S.A. 103, 921–926 (2006). doi: 10.1073/pnas.0504146103 CrossRefGoogle Scholar
  22. 22.
    P.R. Nair, M.A. Alam, Performance limits of nano-biosensors. Appl. Phys. Lett. 88, 233120 (2006)CrossRefGoogle Scholar
  23. 23.
    P.R. Nair, M.A. Alam, Dimensionally frustrated diffusion towards fractal absorbers. Phys. Rev. Lett. 99, 256101 (2007). doi: 10.1103/PhysRevLett.99.256101 CrossRefGoogle Scholar
  24. 24.
    J. Hafner, Ab-initio simulations of materials using VASP: density-functional theory and beyond. J. Comput. Chem. 29, 2044 (2008). doi: 10.1002/jcc.21057 CrossRefGoogle Scholar
  25. 25.
    C. Pisani, L. Maschio, S. Casassa, M. Halo, M. Schutz, D. Usvyat, Periodic local MP2 method for the study of electronic correlation in crystals: theory and preliminary applications. J. Comput. Chem. 29, 2113 (2008). doi: 10.1002/jcc.20975 CrossRefGoogle Scholar
  26. 26.
    A.M.N. Niklasson, Expansion algorithm for the density matrix. Phys. Rev. B 66, 155115 (2002). doi: 10.1103/PhysRevB.66.155115 CrossRefGoogle Scholar
  27. 27.
    L.-W. Wang, Z. Zhao, J. Meza, Linear-scaling three-dimensional fragment method for large-scale electronic structure calculations. Phys. Rev. B 77, 165113 (2008). doi: 10.1103/PhysRevB.77.165113 CrossRefGoogle Scholar
  28. 28.
    W.A. Goddard, A. van Duin, K. Chenoweth, M.-J. Cheng, S. Pudar, J. Oxgaard, B. Merinov, Y.H. Jang, P. Persson, Development of the ReaxFF reactive force field for mechanistic studies of catalytic selective oxidation processes on BiMoOx. Top. Catal. 38, 93–103 (2006). doi: 10.1007/s11244-006-0074-x CrossRefGoogle Scholar
  29. 29.
    A. Laio, F.L. Gervasio, Metadynamics: a method to simulate rare events and reconstruct the free energy in biophysics, chemistry and material science. Rep. Prog. Phys. 71, 126601 (2008). doi: 10.1088/0034-4885/71/12/126601 CrossRefGoogle Scholar
  30. 30.
    T.S. Van Erp, D. Moroni, P.G. Bolhuis, A novel path sampling method for the calculation of rate constants. J. Chem. Phys. 118, 7762–7775 (2003). doi: 10.1063/1.1562614 CrossRefGoogle Scholar
  31. 31.
    S. Datta, Quantum Transport: Atom to Transistor (Cambridge University Press, Cambridge, 2005)Google Scholar
  32. 32.
    C. Kocabas, N. Pimparkar, O. Yesilyurt, S.J. Kang, M.A. Alam, J.A. Rogers, Experimental and theoretical studies of transport through large scale, partially aligned arrays of single-walled carbon nanotubes in thin film type transistors. Nano Lett. 7, 1195–1204 (2007)CrossRefGoogle Scholar
  33. 33.
    R. Schulz, B. Lindner, L. Petridis, J.C. Smith, Scaling of multimillion-atom biological molecular dynamics simulation on a petascale supercomputer. J. Chem. Theory Comput. 5, 2798–2808 (2009)CrossRefGoogle Scholar
  34. 34.
    National Research Council Committee on Modeling, Simulation, and Games, The Rise of Games and High Performance Computing for Modeling and Simulation (The National Academies Press, Washington, DC, 2010). ISBN 978-0-309-14777-4Google Scholar
  35. 35.
    J. Anderson, S.C. Glotzer, Applications of Graphics Processors to Molecular and Nanoscale Simulations, Preprint (2010)Google Scholar
  36. 36.
    M. Garland, S. Le Grand, J. Nickolls, J. Anderson, J. Hardwick, S. Morton, E. Phillips, Y. Zhang, V. Volkov, Parallel computing experiences with CUDA. IEEE Micro 28(4), 13–27, July–August (2008).CrossRefGoogle Scholar
  37. 37.
    J.D. Owens, H. Houston, D. Lubeke, S. Green, J.E. Stone, J.C. Phillips, GPU computing. Proc. IEEE 96, 879–899 (2008). doi: 10.1109/JPROC.2008.917757 CrossRefGoogle Scholar
  38. 38.
    Z. Zhang, P. Fenter, L. Cheng, N.C. Sturchio, M.J. Bedzyk, M. Predota, A. Bandura, J. Kubicki, S.N. Lvov, P.T. Cummings, A.A. Chialvo, M.K. Ridley, P. Bénézeth, L. Anovitz, D.A. Palmer, M.L. Machesky, D.J. Wesolowski, Ion adsorption at the rutile-water interface: linking molecular and macroscopic properties. Langmuir 20, 4954–4969 (2004)CrossRefGoogle Scholar
  39. 39.
    P. Vashishta, R.K. Kalia, A. Nakano, Multimillion atom simulations of dynamics of oxidation of an aluminum nanoparticle and nanoindentation on ceramics. J. Phys. Chem. B 110, 3727–3733 (2006). doi: 10.1021/jp0556153 CrossRefGoogle Scholar
  40. 40.
    S. Izvekov, M. Parrinello, C.J. Burnham, G.A. Voth, Effective force fields for condensed phase systems from ab initio molecular dynamics simulation: a new method for force matching. J. Chem. Phys. 120, 10896 (2004). doi: 10.1063/1.1739396 CrossRefGoogle Scholar
  41. 41.
    D. Reith, M. Pütz, F. Müller-Plathe, Deriving effective mesoscale potentials from atomistic simulations. J. Comput. Chem. 24, 1624–1636 (2003). doi: 10.1002/jcc.10307 CrossRefGoogle Scholar
  42. 42.
    G. Csányi, G. Albaret, G. Moras, M.C. Payne, A. De Vita, Multiscale hybrid simulation methods for material systems. J. Phys. Condens. Matter 17, R691 (2005). doi: 10.1088/0953-8984/17/27/R02 CrossRefGoogle Scholar
  43. 43.
    A. Papavasiliou, I.G. Kevrekidis, Variance reduction for the equation-free simulation of multiscale stochastic systems. Multiscale Model. Simul. 6, 70–89 (2007)CrossRefGoogle Scholar
  44. 44.
    R.W. Hamming, Introduction to Applied Numerical Analysis (from the Introduction) (McGraw-Hill, New York, 1971)Google Scholar
  45. 45.
    M. Kamenetska, S.Y. Quek, A.C. Whalley, M.L. Steigerwald, H.J. Choi, S.G. Louie, C. Nuckolls, M.S. Hybertsen, J.B. Neaton, L. Venkataraman, Conductance and geometry of pyridine-linked single molecule junctions. J. Am. Chem. Soc. 132, 6817 (2010)CrossRefGoogle Scholar
  46. 46.
    M. Stopa, Rectifying behavior in coulomb blockades: charging rectifiers. Phys. Rev. Lett. 88, 146802 (2002)CrossRefGoogle Scholar
  47. 47.
    M. Rontani, F. Troiani, U. Hohenester, E. Molinari, Quantum phases in artificial molecules. Solid State Commun. 119, 309 (2001)CrossRefGoogle Scholar
  48. 48.
    D. Loss, D. DiVincenzo, Quantum computation with quantum dots. Phys. Rev. A 57, 120 (1998). doi: 10.1103/PhysRevA.57.120 CrossRefGoogle Scholar
  49. 49.
    C. Barthel, J. Medford, C.M. Marcus, M.P. Hanson, A.C. Gossard, Interlaced dynamical decoupling and coherent operation of a singlet-triplet Qubit. Phys. Rev. B 81, 161308 (2010) (R)CrossRefGoogle Scholar
  50. 50.
    K. Ono, D.G. Austing, Y. Tokura, S. Tarucha, Current rectification by Pauli exclusion in a weakly coupled double quantum dot system. Science 297, 1313–1317 (2002). doi: 10.1126/science.1070958 CrossRefGoogle Scholar
  51. 51.
    D. Branton, D.W. Deamer, A. Marziali, H. Bayley, S.A. Benner, T. Butler, M. Di Ventra, S. Gara, A. Hibbs, X. Huang, S.B. Jovanovich, P.S. Krstic, S. Lindsay, X.S. Ling, C.H. Mastrangelo, A. Meller, J.S. Oliver, Y.V. Pershin, J.M. Ramsey, R. Riehn, G.V. Soni, V. Tabard-Cossa, M. Wanunu, M. Wiggin, J.A. Schloss, The potential and challenges of nanopore sequencing. Nat. Biotechnol. 26, 1146–1153 (2008)CrossRefGoogle Scholar
  52. 52.
    J.J. Kasianowicz, E. Brandin, D. Branton, D.W. Deamer, Characterization of individual polynucleotide molecules using a membrane channel. Proc. Natl. Acad. Sci. U.S.A. 93, 13770–13773 (1996)CrossRefGoogle Scholar
  53. 53.
    C.M. Payne, X.C. Zhao, L. Vlcek, P.T. Cummings, Molecular dynamics simulation of ss-DNA translocation between copper nanoelectrodes incorporating electrode charge dynamics. J. Phys. Chem. B 112, 1712–1717 (2008)CrossRefGoogle Scholar
  54. 54.
    G. Sigalov, J. Comer, G. Timp, A. Aksimentiev, Detection of DNA sequences using an alternating electric field in a nanopore capacitor. Nano Lett. 8, 56–63 (2008)CrossRefGoogle Scholar
  55. 55.
    W. Timp, U.M. Mirsaidov, D. Wang, J. Comer, A. Aksimentiev, G. Timp, Nanopore sequencing: electrical measurements of the code of life. IEEE Trans. Nanotechnol. 9, 281–294 (2010)CrossRefGoogle Scholar
  56. 56.
    S. Salahuddin, D. Datta, P. Srivastava, S. Datta, Quantum transport simulation of tunneling based spin torque transfer (stt) devices: design trade-offs and torque efficiency. IEEE Electron Dev. Meet. 2007, 121–124 (2007). doi: 10.1109/IEDM.2007.4418879 CrossRefGoogle Scholar
  57. 57.
    O.-S. Lee, G.C. Schatz, Molecular dynamics simulation of DNA-functionalized gold nanoparticles. J. Phys. Chem. C 113, 2316 (2009). doi: 10.1021/jp8094165 CrossRefGoogle Scholar
  58. 58.
    S.Y. Park, A.K.R. Lytton-Jean, B. Lee, S. Weigand, G.C. Schatz, C.A. Mirkin, DNA-programmable nanoparticle crystallization. Nature 451, 553–556 (2008)CrossRefGoogle Scholar
  59. 59.
    A. Gupta, P.R. Nair, D. Akin, M.R. Ladisch, S. Broyles, M.A. Alam, R. Bashir, Anomalous resonance in a nanomechanical biosensor. Proc. Natl. Acad. Sci. U.S.A. 103(36), 13362–13367 (2006)CrossRefGoogle Scholar
  60. 60.
    J. Hahm, C.M. Lieber, Direct ultrasensitive electrical detection of DNA and DNA sequence variations using nanowire nanosensors. Nano Lett. 4(1), 51–54 (2004)CrossRefGoogle Scholar
  61. 61.
    Z.Q. Gao, A. Agarwal, A.D. Trigg, N. Singh, C. Fang, C.-H. Tung, Y. Fan, K.D. Buddharaju, J. Kong, Silicon nanowire arrays for label-free detection of DNA. Anal. Chem. 79(9), 3291–3297 (2007). doi: 10.1021/ac061808q CrossRefGoogle Scholar
  62. 62.
    W. Kusnezow, Y.V. Syagailo, S. Rüffer, K. Klenin, W. Sebald, J.D. Hoheisel, C. Gauer, I. Goychuk, Kinetics of antigen binding to antibody microspots: strong limitation by mass transport to the surface. Proteomics 6(3), 794–803 (2006)CrossRefGoogle Scholar
  63. 63.
    E. Stern, J.F. Klemic, D.A. Routenberg, P.N. Wyrembak, D.B. Turner-Evans, A.D. Hamilton, D.A. LaVan, T.M. Fahmy, M.A. Reed, Label-free immunodetection with CMOS-compatible semiconducting nanowires. Nature 445(7127), 519–522 (2007)CrossRefGoogle Scholar
  64. 64.
    G.F. Zheng, F. Patolsky, Y. Cui, W.U. Wang, C.M. Lieber, Multiplexed electrical detection of cancer markers with nanowire sensor arrays. Nat. Biotechnol. 23(10), 1294–1301 (2005)CrossRefGoogle Scholar
  65. 65.
    E.D. Goluch, J.-M. Nam, D.G. Georganopoulou, T.N. Chiesl, K.A. Shaikh, K.S. Ryu, A.E. Barron, C.A. Mirkin, C. Liu, A bio-barcode assay for on-chip attomolar-sensitivity protein detection. Lab Chip 6(10), 1293–1299 (2006)CrossRefGoogle Scholar
  66. 66.
    J.-M. Nam, S.I. Stoeva, C.A. Mirkin, Bio-bar-code-based DNA detection with PCR-like sensitivity. J. Am. Chem. Soc. 126(19), 5932–5933 (2004)CrossRefGoogle Scholar
  67. 67.
    J.-M. Nam, C.S. Thaxton, C.A. Mirkin, Nanoparticle-based bio-bar codes for the ultrasensitive detection of proteins. Science 301(5641), 1884–1886 (2003). doi: 10.1126/science.1088755 CrossRefGoogle Scholar
  68. 68.
    P.R. Nair, M.A. Alam, Screening-limited response of nanobiosensors. Nano Lett. 8(5), 1281–1285 (2008)CrossRefGoogle Scholar
  69. 69.
    P.R. Nair, M.A. Alam, A theory of “Selectivity” of label-free nanobiosensors: a geometro-physical perspective. J. Appl. Phys. 107, 064701 (2010). doi: 10.1063/1.3310531 CrossRefGoogle Scholar
  70. 70.
    P.T. Cummings, H. Docherty, C.R. Iacovella, J.K. Singh, Phase transitions in nanoconfined fluids: the evidence from simulation and theory. AIChE J. 56, 842–848 (2010). doi: 10.1002/aic.12226 Google Scholar
  71. 71.
    H. Docherty, P.T. Cummings, Direct evidence for fluid-solid transition of nanoconfined fluids. Soft Matter 6, 1640–1643 (2010)CrossRefGoogle Scholar
  72. 72.
    B.E. Kane, A silicon-based nuclear spin quantum computer. Nature 393, 133 (1998)CrossRefGoogle Scholar
  73. 73.
    G.P. Lansbergen, R. Rahman, C.J. Wellard, P.E. Rutten, J. Caro, N. Collaert, S. Biesemans, I. Woo, G. Klimeck, L.C.L. Hollenberg, S. Rogge, Gate induced quantum confinement transition of a single dopant atom in a Si FinFET. Nat. Phys. 4, 656 (2008)CrossRefGoogle Scholar
  74. 74.
    G.P. Lansbergen, C.J. Wellard, J. Caro, N. Collaert, S. Biesemans, G. Klimeck, L.C.L. Hollenberg, S. Rogge, Transport-based dopant mapping in advanced FinFETs. in IEEE IEDM, San Francisco, 15–17 Dec 2008. doi:  10.1109/IEDM.2008.4796794
  75. 75.
    G. Klimeck, S. Ahmed, H. Bae, N. Kharche, R. Rahman, S. Clark, B. Haley, S. Lee, M. Naumov, H. Ryu, F. Saied, M. Prada, M. Korkusinski, T.B. Boykin, Atomistic simulation of realistically sized nanodevices using NEMO 3-D: part I – models and benchmarks. IEEE Trans. Electron Devices 54, 2079–2089 (2007). doi: 10.1109/TED.2007.902879 CrossRefGoogle Scholar
  76. 76.
    D.A. Dixon, P.T. Cummings, K. Hess, Investigative tools: theory, modeling, and simulation (Chap. 2.7.1), in Nanotechnology Research Directions: IWGN Workshop Report. Vision for Nanotechnology in the Next Decade, ed. by M.C. Roco, S. Williams, P. Alivisatos (Kluwer, Dordrecht, 1999)Google Scholar
  77. 77.
    A.C.T. Van Duin, S. Dasgupta, F. Lorant, W.A. Goddard III, ReaxFF: a reactive force field for hydrocarbons. J. Phys. Chem. A 105, 9396–9409 (2001)CrossRefGoogle Scholar
  78. 78.
    D.W. Brenner, O.A. Shenderova, J.A. Harrison, S.J. Stuart, B. Ni, S.B. Sinnott, A second-generation reactive empirical bond order (REBO) potential energy expression for hydrocarbons. J. Phys. Condens. Matter 14, 783 (2002)CrossRefGoogle Scholar
  79. 79.
    T.X.T. Sayle, C.R.A. Catlow, R.R. Maphanga, P.E. Ngoepe, D.C. Sayle, Generating MnO2 nanoparticles using simulated amorphization and recrystallization. J. Am. Chem. Soc. 127, 12828–12837 (2005)CrossRefGoogle Scholar
  80. 80.
    A.M. Walker, B. Slater, J.D. Gale, v Wright, Predicting the structure of screw dislocations in nanoporous materials. Nat. Mater. 3, 715–720 (2004). doi: 10.1038/nmat1213 CrossRefGoogle Scholar
  81. 81.
    S. Piana, M. Reyhani, J.D. Gale, Simulating micrometer-scale crystal growth from solution. Nature 438, 70 (2005). doi: 10.1038/nature04173 CrossRefGoogle Scholar
  82. 82.
    P. Murray-Rust, H.S. Rzepa, Chemical markup, XML, and the worldwide web. 1. Basic principles. J. Chem. Inf. Comput. Sci. 39, 928 (1999). doi: 10.1021/ci990052B Google Scholar
  83. 83.
    W.A. De Jong, Utilizing high performance computing for chemistry: parallel computational chemistry. Phys. Chem. Chem. Phys. 12, 6896 (2010). doi: 10.1039/c002859b CrossRefGoogle Scholar
  84. 84.
    P.T. Cummings, S.C. Glotzer, Inventing a New America Through Discovery and Innovation in Science, Engineering and Medicine: A Vision for Research and Development in Simulation-Based Engineering and Science in the Next Decade (World Technology Evaluation Center, Baltimore, 2010)Google Scholar

Copyright information

© Springer Science+Business B.V. 2011

Authors and Affiliations

  1. 1.School of Electrical and Computer EngineeringPurdue UniversityWest LafayetteUSA
  2. 2.Vanderbilt UniversityNashvilleUSA

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