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

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Abstract

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.

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References

  1. Rieth M, Schommers W (2006) Atomic nanodesign, handbook of theoretical and computational nanotechnology. American Scientific Publishers, Valencia

    Google Scholar 

  2. Torrens M (1972) Interatomic potentials. Academic Press, New York

    Google Scholar 

  3. Daw MS, Hatcher R (1985) Application of the embedded atom method to phonons in transition metals. Solid State Commun 56:697–699

    Article  Google Scholar 

  4. Foiles SM (1985) Application of the embedded-atom method to liquid transition metal. Phys Rev B 32:3409–3415

    Article  Google Scholar 

  5. Daw MS (1986) Surface reconstruction and many-atom models. Surf Sci 166:L161–L164

    Article  Google Scholar 

  6. Arregui EO, Caro M, Caro A (2002) Characterizing many-body localization by out-of-time-ordered correlation. Phys Rev B 6:054201

    Article  Google Scholar 

  7. Schommers W (1975) The volocity autocorrelation function and the effect on the long-range interaction in liquid rubidium. Solid State Commun 16:45–47

    Article  Google Scholar 

  8. Lüscher E (1973) in Die feste Materie, Umschau Verlag, Frankfurt am Main

    Google Scholar 

  9. Morse PM (1929) Diatomic molecules according to the wave mechanics. Phys Rev 34:57–60

    Article  Google Scholar 

  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 29:3117–3122

    Article  Google Scholar 

  11. Eichler M, Peyzl M (1969) Intrinsic stacking faults on 112 planes in the B.C.C. Lattice Phys Stat. Sol 35:333–338

    Article  Google Scholar 

  12. Lincoln RG, Koliwad KM, Ghate PB (1967) Morse-potential evaluation of elastic constants of some cubic metals. Phys Rev. 157:463–466

    Article  Google Scholar 

  13. Schommers W (1999) Excited nano-clusters. Appl. Phys. A 68:187–196

    Article  Google Scholar 

  14. Schommers W (1976) The Effect of van der Waals-Type interactions in metals: a pseudopotential Model. Z. Phys. B 24:171

    Article  Google Scholar 

  15. Schommers W (1997) Phonons and structure in nano-clusters: a molecular dynamics study for Al. Nanostruct. Materials 9:693–696

    Article  Google Scholar 

  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, London

    Google Scholar 

  17. Rescher N (1977) The limits of science. University of California Press, Berkeley

    Google Scholar 

  18. Schommers W (2011) Quantum processes. Word Scientific, London

    Book  Google Scholar 

  19. Debi A, Anitha A (2017) A comparative study of statistical and rough computing models in predictive data analysis. Int J Ambient Comput Intell 8:32–51

    Article  Google Scholar 

  20. Kamal S, Ripon SH, Dey N, Ashour AS, Santhi V (2016) Comput Mehods Programs Biomed 131:32

    Google Scholar 

  21. Zhang W, Qi Q, Deng J (2017) Building intelligent transportation cloud data center based on SOA. Int J Ambient Comput Intell 8:1–11

    Article  Google Scholar 

  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, Berlin

    Google Scholar 

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Correspondence to Wolfram Schommers .

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Schommers, W. (2019). Structure and Dynamics of Many-Particle Systems: Big Data Sets and Data Analysis. In: Dey, N., Bhatt, C., Ashour, A. (eds) Big Data for Remote Sensing: Visualization, Analysis and Interpretation. Springer, Cham. https://doi.org/10.1007/978-3-319-89923-7_3

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