Structure and Dynamics of Many-Particle Systems: Big Data Sets and Data Analysis



In this chapter it is underlined that the structure and dynamics of many-particle systems determine essentially the properties of the systems in solid state physics, materials science and nano-technology. The fundamental description of such systems is based on atoms or molecules that interact with each other. The physical background is discussed in detail. In particular, it is argued that the characteristic features and properties of such systems are already reflected by relatively small parts consisting of 102–107 particles. It is outlined that for all the systems used in solid state physics etc. no reliable analytical models exist and we have to recourse to numerical methods. The procedure is to solve Newton’s equations of motion numerically using the interaction potential as input, and these fundamental equations of motion are expressed by coupled differential equations (molecular dynamics). The essential features of the molecular dynamics method have been discussed. The basic information is very large and we get in such investigations “big data sets”, and this information is normally used for data analysis. For the production of the big data sets and their analysis sufficiently large and fast computers are necessary. Does nature also produce big data sets for its operations? This question is discussed critically.


Many-particle systems Molecular dynamics Predictor-corrector algorithm Data reduction Data classification Nano-systems Basic reality 


  1. 1.
    Rieth M, Schommers W (2006) Atomic nanodesign, handbook of theoretical and computational nanotechnology. American Scientific Publishers, ValenciaGoogle Scholar
  2. 2.
    Torrens M (1972) Interatomic potentials. Academic Press, New YorkGoogle Scholar
  3. 3.
    Daw MS, Hatcher R (1985) Application of the embedded atom method to phonons in transition metals. Solid State Commun 56:697–699CrossRefGoogle Scholar
  4. 4.
    Foiles SM (1985) Application of the embedded-atom method to liquid transition metal. Phys Rev B 32:3409–3415CrossRefGoogle Scholar
  5. 5.
    Daw MS (1986) Surface reconstruction and many-atom models. Surf Sci 166:L161–L164CrossRefGoogle Scholar
  6. 6.
    Arregui EO, Caro M, Caro A (2002) Characterizing many-body localization by out-of-time-ordered correlation. Phys Rev B 6:054201CrossRefGoogle Scholar
  7. 7.
    Schommers W (1975) The volocity autocorrelation function and the effect on the long-range interaction in liquid rubidium. Solid State Commun 16:45–47CrossRefGoogle Scholar
  8. 8.
    Lüscher E (1973) in Die feste Materie, Umschau Verlag, Frankfurt am MainGoogle Scholar
  9. 9.
    Morse PM (1929) Diatomic molecules according to the wave mechanics. Phys Rev 34:57–60CrossRefGoogle Scholar
  10. 10.
    Mohammed KMM, Shukla F, Milstein J, Merz L (1984) Resonant phonon-assisted energy transfer in ruby from 29-cm−1-phonon dynamics. Phys Rev 29:3117–3122CrossRefGoogle Scholar
  11. 11.
    Eichler M, Peyzl M (1969) Intrinsic stacking faults on 112 planes in the B.C.C. Lattice Phys Stat. Sol 35:333–338CrossRefGoogle Scholar
  12. 12.
    Lincoln RG, Koliwad KM, Ghate PB (1967) Morse-potential evaluation of elastic constants of some cubic metals. Phys Rev. 157:463–466CrossRefGoogle Scholar
  13. 13.
    Schommers W (1999) Excited nano-clusters. Appl. Phys. A 68:187–196CrossRefGoogle Scholar
  14. 14.
    Schommers W (1976) The Effect of van der Waals-Type interactions in metals: a pseudopotential Model. Z. Phys. B 24:171CrossRefGoogle Scholar
  15. 15.
    Schommers W (1997) Phonons and structure in nano-clusters: a molecular dynamics study for Al. Nanostruct. Materials 9:693–696CrossRefGoogle Scholar
  16. 16.
    Rieth M (2003) In: Politis C, Schommers W (eds) Nano-engineering in science and technology, series on the foundations of natural science and technology, vol 6. World Scientific, New Jersey, LondonGoogle Scholar
  17. 17.
    Rescher N (1977) The limits of science. University of California Press, BerkeleyGoogle Scholar
  18. 18.
    Schommers W (2011) Quantum processes. Word Scientific, LondonCrossRefGoogle Scholar
  19. 19.
    Debi A, Anitha A (2017) A comparative study of statistical and rough computing models in predictive data analysis. Int J Ambient Comput Intell 8:32–51CrossRefGoogle Scholar
  20. 20.
    Kamal S, Ripon SH, Dey N, Ashour AS, Santhi V (2016) Comput Mehods Programs Biomed 131:32Google Scholar
  21. 21.
    Zhang W, Qi Q, Deng J (2017) Building intelligent transportation cloud data center based on SOA. Int J Ambient Comput Intell 8:1–11CrossRefGoogle Scholar
  22. 22.
    Dey N, Hassanie AE, Bhatt C, Ashour A, Satapathy SC (eds) (2017) Internet of Things and big data analytics toward next-generation intelligence. Springer, BerlinGoogle Scholar

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© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  1. 1.University of Texas at ArlingtonArlingtonUSA

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