Monte Carlo Approach for Modeling and Optimization of One-Dimensional Bimetallic Nanostructures

  • Vladimir MyasnichenkoEmail author
  • Nickolay Sdobnyakov
  • Leoneed KirilovEmail author
  • Rossen Mikhov
  • Stefka Fidanova
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11189)


In this paper we present a method for optimizing of metal nanoparticle structures. The core of the method is a lattice Monte-Carlo method with different lattices combined with an approach from molecular dynamics. Interaction between atoms is calculated using multi-particle tight-binding potential of Gupta – Cleri&Rosato. The method allows solving of problems with periodic boundary conditions. It can be used for modeling of one-dimensional (nanowire, tube) and two-dimensional (nano-film) structures. If periodic boundary conditions are not given, we assume finite dimensions of the model lattice. In addition, automatic relaxation of the crystal lattice can be performed in order to minimize further the potential energy of the system. Both stretching and compressing of the lattice is permitted. A computer implementation of the method is developed. It allows easy and efficient operation. It uses the commonly accepted XYZ format for describing metal nanoparticles. The parameters of the method, such as number and type of metal atoms, temperature of the system, etc. are entered on a separate command line. The method is tested extensively on a large set of examples.



This research is supported by the Russian Foundation for Basic Research project No. 18-38-00571 mol_a and the Bulgarian NSF under the grant DFNI-DN 12/5.


  1. 1.
    Sannicolo, T., Lagrange, M., Cabos, A., et al.: Metallic nanowire-based transparent electrodes for next generation flexible devices: a review. Small 12(44), 6052–6075 (2016)CrossRefGoogle Scholar
  2. 2.
    Luo, M., Liu, Y., Huang, W., Qiao, W.: Towards flexible transparent electrodes based on carbon and metallic materials. Micromachines 8(1), 12 (2017)CrossRefGoogle Scholar
  3. 3.
    Li, H., Biser, J.M., Perkins, J.T., Dutta, S., et al.: Thermal stability of Cu nanowires on a sapphire substrate. J. Appl. Phys. 103(2), 024315-1–024315-9 (2008)Google Scholar
  4. 4.
    Langley, D.P., Lagrange, M., Giusti, G., Jiménez, C., et al.: Metallic nanowire networks: effects of thermal annealing on electrical resistance. Nanoscale 6(22), 13535–13543 (2014)CrossRefGoogle Scholar
  5. 5.
    Karim, S., Toimil-Molares, M.E., Balogh, A.G., et al.: Morphological evolution of Au nanowires controlled by Rayleigh instability. Nanotechnology 17(24), 5954–5959 (2006)CrossRefGoogle Scholar
  6. 6.
    Rauber, M., Muench, F., Toimil-Molares, M.E., Ensinger, W.: Thermal stability of electrodeposited platinum nanowires and morphological transformations at elevated temperatures. Nanotechnology 23(47), 475710 (2012)CrossRefGoogle Scholar
  7. 7.
    Granberg, F., Parviainen, S., Djurabekova, F., Nordlund, K.: Investigation of the thermal stability of Cu nanowires using atomistic simulations. J. Appl. Phys. 115(21), 213518-1–213518-5 (2014)CrossRefGoogle Scholar
  8. 8.
    Calvo, F.: Solid-solution precursor to melting in onion-ring Pd-Pt nanoclusters: a case of second-order-like phase change? Faraday Discuss. 138, 75–88 (2008)CrossRefGoogle Scholar
  9. 9.
    Davis, J., Johnston, R., Rubinovich, L., Polak, M.: Comparative modelling of chemical ordering in palladium-iridium nanoalloys. J. Chem. Phys. 141, 224307 (2014)CrossRefGoogle Scholar
  10. 10.
    Ferrando, R., Fortunelli, A., Johnston, R.: Searching for the optimum structures of alloy nanoclusters. Phys. Chem. Chem. Phys. 10, 640–649 (2008)CrossRefGoogle Scholar
  11. 11.
    Panizon, E., Olmos-Asar, J., Peressi, M., Ferrando, R.: The study of the structure and thermodynamics of CuNi nanoalloys using a new DFT-fitted atomistic potential. Phys. Chem. Chem. Phys. 17, 28068–28075 (2015)CrossRefGoogle Scholar
  12. 12.
    Parsina, I., DiPaola, C., Baletto, F.: A novel structural motif for free CoPt nanoalloys. Nanoscale 4, 1160–1166 (2012)CrossRefGoogle Scholar
  13. 13.
    Paz-Borbón, L., Mortimer-Jones, Th, Johnston, R., Posada-Amarillas, A., et al.: Structures and energetics of 98 atom Pd–Pt nanoalloys: potential stability of the Leary tetrahedron for bimetallic nanoparticles. Phys. Chem. Chem. Phys. 9, 5202–5208 (2007)CrossRefGoogle Scholar
  14. 14.
    Schebarchov, D., Wales, D.: A new paradigm for structure prediction in multicomponent systems. J. Chem. Phys. 139(22), 221101 (2013). Scholar
  15. 15.
    Schebarchov, D., Wales, D.: Quasi-combinatorial energy landscapes for nanoalloy structure optimization. Phys. Chem. Chem. Phys. 17, 28331–28338 (2015)CrossRefGoogle Scholar
  16. 16.
    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 (2015)CrossRefGoogle Scholar
  17. 17.
    Toai, T.J., Rossi, G., Ferrando, R.: Global optimisation and growth simulation of AuCu clusters. Faraday Discuss. 138, 49–58 (2008). Scholar
  18. 18.
    Bilalbegović, G.: Structures and melting in infinite gold nanowires. Solid State Commun. 115, 73–76 (2000)CrossRefGoogle Scholar
  19. 19.
    Liu, W., Chen, P., Qiu, R., Khan, M., et al.: A molecular dynamics simulation study of irradiation induced defects in gold nanowire. Nucl. Instrum. Methods Phys. Res. Sect. B Beam Interact. Mater. Atoms. 405, 22–30 (2017)CrossRefGoogle Scholar
  20. 20.
    Diao, J., Gall, K., Dunn, M.L., Zimmerman, J.A.: Atomistic simulations of the yielding of gold nanowires. Acta Mater. 54(3), 643–653 (2006)CrossRefGoogle Scholar
  21. 21.
    Zepeda-Ruiz, L.A., Sadigh, B., Biener, J., Hodge, A.M., et al.: Mechanical response of freestanding Au nanopillars under compression. Appl. Phys. Lett. 91(10), 101907-1–101907-3 (2007)CrossRefGoogle Scholar
  22. 22.
    Olsson, P.A.T., Park, H.S.: Atomistic study of the buckling of gold nanowires. Acta Mater. 59(10), 3883–3894 (2011)CrossRefGoogle Scholar
  23. 23.
    He, X., Cheng, F., Chen, Z.-X.: The lattice kinetic Monte Carlo simulation of atomic diffusion and structural transformation for gold. Sci. Rep. 6(1), 33128 (2016)CrossRefGoogle Scholar
  24. 24.
    Baibuz, E., Vigonski, S., Lahtinena, J., Zhao, J., Jansson, V., Zadin, V., Djurabekova, F.: Migration barriers for surface diffusion on a rigid lattice: challenges and solutions. Comput. Mater. Sci. 146, 287–302 (2018). Scholar
  25. 25.
    Cleri, F., Rosato, V.: Tight-binding potentials for transition metals and alloys. Phys. Rev. B 48, 22–33 (1993)CrossRefGoogle Scholar
  26. 26.
    Sutton, A., Chen, J.: Long-range Finnis-Sinclair potentials. Philos. Mag. Lett. 61, 139–146 (1990)CrossRefGoogle Scholar
  27. 27.
    Myshlavtsev, A.V., Stishenko, P.V.: Modification of the metropolis algorithm for modeling metallic nanoparticles. Omsk Sci. Newsp. 1(107), 21–25 (2012). (in Russian)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Tver State UniversityTverRussia
  2. 2.Institute of Information and Communication TechnologiesBulgarian Academy of SciencesSofiaBulgaria

Personalised recommendations