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Structural and Thermodynamic Properties of Au2–58 Clusters

  • Yi DongEmail author
  • Michael Springborg
  • Ingolf Warnke
Part of the Progress in Theoretical Chemistry and Physics book series (PTCP, volume 27)

Abstract

In this study, we have used a parametrized density-functional tight-binding method combined with genetic algorithms for an unbiased global optimization to study systematically neutral gold clusters with from 2 to 58 atoms. The ground states of the clusters are identified and different descriptors are used to analyze the properties of the clusters, including stability, overall shape, similarity, growth patterns, and structural motifs. The vibrational heat capacity of the ground state of neutral gold clusters at different temperatures are calculated by a newly developed method. The results show that the heat capacity is strongly size-dependent, particularly at low temperature.

Keywords

Genetic Algorithm Heat Capacity Stability Function Gold Cluster Vibrational Property 
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.

Notes

Acknowledgements

This work was supported by the German Research Council (DFG) through project Sp439/23.

References

  1. 1.
    Rossi G, Ferrando R (2006) Chem Phys Lett 423:17 CrossRefGoogle Scholar
  2. 2.
    Donnelly RA (1994) Chem Phys Lett 136:274 CrossRefGoogle Scholar
  3. 3.
    Wales DJ, Doye JPK (1997) J Phys Chem 101:5111 CrossRefGoogle Scholar
  4. 4.
    Deaven DM, Ho KM (1995) Phys Rev Lett 75:288 CrossRefGoogle Scholar
  5. 5.
    Morris JR, Deaven DM, Ho KM (1996) Phys Rev B 53:R1740 CrossRefGoogle Scholar
  6. 6.
    Deaven DM, Tit N, Morris JR, Ho KM (1996) Chem Phys Lett 256:195 CrossRefGoogle Scholar
  7. 7.
    Ge Y-B, Head JD (2004) Chem Phys Lett 398:107 CrossRefGoogle Scholar
  8. 8.
    Ge Y-B, Heas JD (2004) J Phys Chem B 108:6025 CrossRefGoogle Scholar
  9. 9.
    Joswig J-O, Springborg M (2003) Phys Rev B 68:085408 CrossRefGoogle Scholar
  10. 10.
    Baletto F, Ferrando R (2005) Rev Mod Phys 77:371 CrossRefGoogle Scholar
  11. 11.
    Fa W, Luo C, Dong J (2005) J Phys Rev B 72:205428 CrossRefGoogle Scholar
  12. 12.
    Schmidt M, Donger J, Hippler T, Haberland H (2003) Phys Rev Lett 90:103401 CrossRefGoogle Scholar
  13. 13.
    Calvo F, Spiegelman F (2004) J Chem Phys 120:9684 CrossRefGoogle Scholar
  14. 14.
    Breaux GA, Neal CM, Cao B-P, Jarrold MF (2005) Phys Rev Lett 94:173401 CrossRefGoogle Scholar
  15. 15.
    Dong Y, Springborg M (2007) J Phys Chem C 111:12528 CrossRefGoogle Scholar
  16. 16.
    Seifert G, Porezag D, Fraunheim Th (1996) Int J Quant Chem 58:185 CrossRefGoogle Scholar
  17. 17.
    Dong Y, Burkhart M, Veith M, Springborg M (2005) J Phys Chem B 109:22820 CrossRefGoogle Scholar
  18. 18.
    Goldberg DE (1986) Genetic algorithms in search optimization and machine learning. Addison-Wesley, Reading Google Scholar
  19. 19.
    Bowman JM (1986) Acc Chem Res 19:202 CrossRefGoogle Scholar
  20. 20.
    Hunnycutt JD, Andersen AC (1987) J Phys Chem 91:4950 CrossRefGoogle Scholar
  21. 21.
    Clarke AS, Jónsson H (1993) Phys Rev E 47:3795 CrossRefGoogle Scholar
  22. 22.
    Faken D, Jónsson H (1994) Comput Mater Sci 2:279 CrossRefGoogle Scholar
  23. 23.
    Bulusu S, Li X, Wang L-S, Zeng XC (2006) Proc Natl Sci 103:8326 CrossRefGoogle Scholar
  24. 24.
    Dong Y, Springborg M, Pang Y, Morillon FM. Comput Theor Chem, in press. doi: 10.1016/j.comptc.2013.06.004
  25. 25.
    Dong Y, Springborg M, Warnke I (2011) Theor Chem Acc 130:1001 CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  1. 1.Physical and Theoretical ChemistryUniversity of SaarlandSaarbrückenGermany
  2. 2.Department of ChemistryYale UniversityNew HavenUSA

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