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Global Ab Initio Simulations: Study of A Liquid As An Example

  • Enrico Clementi
  • G. C. Lie
  • L. Hannon
  • D. C. Rapaport
  • M. Wojcik

Abstract

By now it is a rather accepted viewpoint that science can be divided into “laboratory-experimental” and “theoretical-computational.” Possibly, however, it is not sufficiently realized that computational science has made “enormous” progress in the last few years because of the availability of very fast, high performance computers, generally referred to as supercomputers. In referring to the “tremendous progress,” we do not wish to put our emphasis on the size and the magnitude of the computations, but rather on the viewpoint. Indeed, now we can think in terms of “global simulations,” namely simulations of a full problem, no longer limited to a specific subspecialized field. Let us clarify with an example related to chemistry and physics; when we consider the motions of an ensemble of molecules of water, we can either think in terms of the motions of the atoms within a single molecule or the motion of one molecule of water within the solvation cavity of other molecules of water (namely within the solvation cell), or we can think in terms of the collective motions where many solvation cells are interacting one with another. We can even go one step further and think in terms of very large systems where traditionally one would use fluid dynamics rather than a discrete representation like quantum or statistical mechanics. In other words, because of supercomputers we are able to analyze an ensemble of molecules of water from quantum, to statistical, to fluid dynamics. In this paper we shall demonstrate the feasibility of our global approach by presenting a detailed discussion on simulations of liquid water. This is likely the first application of the global viewpoint but surely more and more will follow.

Keywords

Liquid Water Pair Correlation Function Macroscopic Theory Velocity Autocorrelation Function Acceleration Field 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Plenum Press, New York 1986

Authors and Affiliations

  • Enrico Clementi
    • 1
    • 2
  • G. C. Lie
    • 1
    • 2
  • L. Hannon
    • 1
    • 2
  • D. C. Rapaport
    • 1
    • 2
  • M. Wojcik
    • 1
    • 2
  1. 1.Data Systems Division, Dept. 48B/MS 428IBM CorporationKingstonUSA
  2. 2.BethesdaUSA

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