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Journal of Statistical Physics

, Volume 171, Issue 3, pp 427–433 | Cite as

The Global Optimization of Pt13 Cluster Using the First-Principle Molecular Dynamics with the Quenching Technique

  • Xiangping Chen
  • Haiming Duan
  • Biaobing Cao
  • Mengqiu Long
Article
  • 132 Downloads

Abstract

The high-temperature first-principle molecular dynamics method used to obtain the low energy configurations of clusters [L. L. Wang and D. D. Johnson, PRB 75, 235405 (2007)] is extended to a considerably large temperature range by combination with the quenching technique. Our results show that there are strong correlations between the possibilities for obtaining the ground-state structure and the temperatures. Larger possibilities can be obtained at relatively low temperatures (as corresponds to the pre-melting temperature range). Details of the structural correlation with the temperature are investigated by taking the Pt13 cluster as an example, which suggests a quite efficient method to obtain the lowest-energy geometries of metal clusters.

Keywords

Molecular dynamics The first-principle calculation Global optimization Metal clusters 

Notes

Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant No. 11664038).

References

  1. 1.
    Baletto, F., Ferrando, R.: Structural properties of nanoclusters: energetic, thermodynamic, and kinetic effects. Rev. Mod. Phys. 77, 371–423 (2005)ADSCrossRefGoogle Scholar
  2. 2.
    Wales, D.J., Scheraga, H.A.: Global optimization of clusters, crystals, and biomolecules. Science 285, 1368–1372 (1999)CrossRefGoogle Scholar
  3. 3.
    Yoo, S., Zeng, X.C.: Global geometry optimization of silicon clusters described by three empirical potentials. J. Chem. Phys. 119, 1442–1450 (2003)ADSCrossRefGoogle Scholar
  4. 4.
    Wales, D.J., Hodges, M.P.: Global minima of water cluster (H2O)n, n ≤ 21, described by an empirical potential. Chem. Phys. Lett. 286, 65–75 (1998)ADSCrossRefGoogle Scholar
  5. 5.
    Yeo, S.C., Kim, D.H., Shin, K., Lee, H.M.: Phase diagram and structural evolution of Ag-Au bimetallic nanoparticles: molecular dynamics simulations. Phys. Chem. Chem. Phys. 14, 2791–2796 (2012)CrossRefGoogle Scholar
  6. 6.
    Rapallo, A., Rossi, G., Ferrando, R.: Global optimization of bimetallic cluster structures. I. Size-mismatched Ag-Cu, Ag-Ni, and Au-Cu systems. J. Chem. Phys. 122, 194308 (2005)ADSCrossRefGoogle Scholar
  7. 7.
    Shayeghi, A., Götz, D., Davis, J.B.A., Schäfer, R., Johnston, R.L.: Pool-BCGA: a parallelised generation-free genetic algorithm for the ab initio global optimisation of nanoalloy clusters. Phys. Chem. Chem. Phys. 17, 2104–2112 (2015)CrossRefGoogle Scholar
  8. 8.
    Davis, J.B.A., Horswell, S.L., Johnston, R.L.: Global optimization of 8 − 10 atom palladium − iridium nanoalloys at the DFT level. J. Phys. Chem. A 118, 208–214 (2014)CrossRefGoogle Scholar
  9. 9.
    Datta, S., Raychaudhuri, A.K., Dasgupta, T.S.: First principles study of bimetallic Ni13-nAgn nano-clusters (n = 0–13): structural, mixing, electronic, and magnetic properties. J. Chem. Phys. 146, 164301–164308 (2017)ADSCrossRefGoogle Scholar
  10. 10.
    Zhang, M., Fournier, R.: Density-functional-theory study of 13-atom metal clusters M13, M = Ta-Pt. Phys. Rev. A 79, 043203–043210 (2009)ADSCrossRefGoogle Scholar
  11. 11.
    Chou, J.P., Hsing, C.R., Wei, C.M.: Ab initio random structure search for 13-atom cluster of fcc elements. J. Phys. Condens. Mat. 25, 125305–125307 (2013)ADSCrossRefGoogle Scholar
  12. 12.
    Wales, D.J., Doye, J.P.K.: On the thermodynamics of global optimization. J. Phys. Chem. A 101, 5111 (1997)CrossRefGoogle Scholar
  13. 13.
    Wales, D.J., Bogdan, T.V.: Potential energy and free energy landscapes. J. Phys. Chem. B 110, 20765 (2006)CrossRefGoogle Scholar
  14. 14.
    Gehrke, R., Reuter, K.: Assessing the efficiency of first-principles basin-hopping sampling. Phys. Rev. B 79, 085412 (2009)ADSCrossRefGoogle Scholar
  15. 15.
    Rondina, G.G., Silva, J.L.F.D.: Revised basin-hopping Monte Carlo algorithm for structure optimization of clusters and nanoparticles. J. Chem. Inf. Model. 53, 2282–2298 (2013)CrossRefGoogle Scholar
  16. 16.
    Hartke, B.: Global geometry optimization of clusters using genetic algorithms. J. Phys. Chem. 97, 9973–9976 (1993)CrossRefGoogle Scholar
  17. 17.
    Deaven, D.M., Ho, K.M.: Molecular geometry optimization with a genetic algorithm. Phys. Rev. Lett. 75, 288 (1995)ADSCrossRefGoogle Scholar
  18. 18.
    Daven, D., Tit, N., Morris, J., Ho, K.: Structural optimization of Lennard-Jones clusters by a genetic algorithm. Chem. Phys. Lett. 256, 195–200 (1996)ADSCrossRefGoogle Scholar
  19. 19.
    Wang, L.L., Johnson, D.D.: Density functional study of structural trends for late-transition-metal 13-atom clusters. Phys. Rev. B 75, 235405–235410 (2007)ADSCrossRefGoogle Scholar
  20. 20.
    Hu, C.H., Chizallet, C., Toulhoat, H., Raybaud, P.: Structural, energetic, and electronic trends in low-dimensional late-transition-metal systems. Phys. Rev. B 79, 195416 (2009)ADSCrossRefGoogle Scholar
  21. 21.
    Piotrowski, M.J., Piquini, P., Da Silva, J.L.F.: Density functional theory investigation of 3d, 4d, and 5d 13-atom metal clusters. Phys. Rev. B 81, 155446 (2010)ADSCrossRefGoogle Scholar
  22. 22.
    Kresse, G., Furthmüller, J.: Efficiency of ab initio total energy calculations for metals and semiconductors using a plane-wave basis set. Comp. Mater. Sci. 6, 15–50 (1996)CrossRefGoogle Scholar
  23. 23.
    Kresse, G., Furthmüller, J.: Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set. Phys. Rev. B 54, 11169–11186 (1996)ADSCrossRefGoogle Scholar
  24. 24.
    Kresse, G., Joubert, D.: From ultrasoft pseudopotentials to the projector augmented-wave method. Phys. Rev. B 59, 1758–1775 (1999)ADSCrossRefGoogle Scholar
  25. 25.
    Kohn, W., And, A.D.B., Parr, R.G.: Density functional theory of electronic structure. J. Phys. Chem. A 31, 12974–12980 (1996)CrossRefGoogle Scholar
  26. 26.
    Görling, A.: Density-functional theory beyond the Hohenberg-Kohn theorem. Phys. Rev. A 59, 3359–3374 (1999)ADSCrossRefGoogle Scholar
  27. 27.
    Nosè, S.: S.: a unified formulation of the constant temperature molecular dynamics methods. J. Chem. Phys. 81, 511–519 (1984)ADSCrossRefGoogle Scholar
  28. 28.
    Piotrowski, M.J., Piquini, P., Zeng, Z.H., Da Silva, J.L.F.: Adsorption of NO on the Rh13, Pd13, Ir13, and Pt13 clusters: a density functional theory investigation. J. Phys. Chem. C 116, 20540–20549 (2012)CrossRefGoogle Scholar

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Authors and Affiliations

  1. 1.College of Physics Science and TechnologyXinjiang UniversityUrumqiPeople’s Republic of China
  2. 2.Hunan Key Laboratory of Super Micro-structure and Ultrafast ProcessCentral South UniversityChangshaPeople’s Republic of China

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