A Two-Stage Monte Carlo Approach for Optimization of Bimetallic Nanostructures

• Autorzy: Mikhov, Rossen , Myasnichenko, Vladimir , Kirilov, Leoneed , Sdobnyakov, Nickolay , Matrenin, Pavel , Sokolov, Denis , Fidanova, Stefka
• Konferencja: Federated Conference on Computer Science and Information Systems (15 ; 06-09.09.2020 ; Sofia, Bulgaria)
• Języki publikacji: EN

Abstrakty

EN

In this paper we propose a two-stage lattice Monte Carlo approach for optimization of bimetallic nanoalloys: simulated annealing on a larger lattice, followed by simulated diffusion. Both algorithms are fairly similar in structure, but their combination was found to give significantly better solutions than simulated annealing alone. We also discuss how to tune the parameters of the algorithms so that they work together optimally.

Słowa kluczowe

PL

dyfuzja; Metody Monte Carlo; materiały nanostrukturalne; symulowane wyżarzanie

EN

diffusion; Monte Carlo methods; nanostructured materials; simulated annealing

• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2020
• Tom: Vol. 21
• Strony: 285--288
• Opis fizyczny: Bibliogr. 16 poz., wz., il., wykr., tab.

Twórcy

autor

Mikhov, Rossen

• Bulgarian Academy of Sciences, Acad. G. Bonchev Str., bl. 2, 1113 Sofia, Bulgaria

autor

Myasnichenko, Vladimir

• Tver State University 33, Zhelyabova Str. 170100 Tver, Russia,

autor

Kirilov, Leoneed

• Bulgarian Academy of Sciences, Acad. G. Bonchev Str., bl. 2, 1113 Sofia, Bulgaria,

autor

Sdobnyakov, Nickolay

• Tver State University 33, Zhelyabova Str. 170100 Tver, Russia

autor

Matrenin, Pavel

• Novosibirsk State Technical University 20, Prospekt K. Marksa, 630087 Novosibirsk, Russia

autor

Sokolov, Denis

• Tver State University 33, Zhelyabova Str. 170100 Tver, Russia

autor

Fidanova, Stefka

• Bulgarian Academy of Sciences, Acad. G. Bonchev Str., bl. 2, 1113 Sofia, Bulgaria

Bibliografia

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• 2. X. Wu and Y. Sun, “Stable structures and potential energy surface of the metallic clusters: Ni, Cu, Ag, Au, Pd, and Pt,” J. Nanopart Res., vol. 19, art. no. 201, July 2017.
• 3. K. Michaelian, N. Rendón, and I. L. Garzón, “Structure and energetics of Ni, Ag, and Au nanoclusters,” Phys. Rev. B, vol. 60, no. 3, pp. 2000–2010, July 1999.
• 4. J. P. K. Doye, “Physical perspectives on the global optimization of atomic clusters,” in Global Optimization. Nonconvex Optimization and Its Applications, vol. 85, J. D. Pintér, Ed. Boston, MA: Springer, 2006, pp. 103–139.
• 5. S. B. Gelfand and S. K. Mitter, “Metropolis-type annealing algorithms for global optimization in {R}^d,” SIAM J. Control Optim., vol. 31, no. 1, pp. 111–131, 1993.
• 6. M. C. Giménez and W. Schmicker, “Monte Carlo simulation of nanowires of different metals and two-metal alloys,” J. Chem. Phys., vol. 134, pp. 064707-1–064707-6, Febr. 2011.
• 7. F. Calvo, D. Schebarchov, and D. J. Wales, “Grand and semigrand canonical basin-hopping,” J. Chem. Theory Comput., vol. 12, no. 2, pp. 902–909, Dec. 2015.
• 8. H. G. Kim, S. K. Choi, and H.M. Lee, “New algorithm in the basin hopping Monte Carlo to find the global minimum structure of unary and binary metallic nanoclusters,” J. Chem. Phys., vol. 128, no. 14, pp.144702-1–144702-4, Apr. 2008.
• 9. C. Chen, Y. Zuo, W. Ye, et al., “Critical review of machine learning of energy materials,” Adv. Energy Mater., vol. 10, no. 8, 1903242-1–1903242-36, Jan. 2020.
• 10. S. Balduin, F. Oest, M. Blank-Babazadeh, A. Nieße, and S. Lehnhoff, “Tool-assisted surrogate selection for simulation models in energy systems,” in Proc. 2019 FedCSIS, pp. 185–192.
• 11. F. Cleri and V. Rosato, “Tight-binding potentials for transition metals and alloys,” Phys. Rev. B, vol. 48, no. 1, pp. 22–33, July 1993.
• 12. V. Myasnichenko, N. Sdobnyakov, L. Kirilov, R. Mikhov, and S. Fidanova, “Structural instability of gold and bimetallic nanowires using Monte Carlo simulation,” in Recent Advances in Computational Optimization: Results of the Workshop on Computational Optimization and Numerical Search and Optimization 2018, S. Fidanova, Ed. Springer, 2020, pp. 133–145.
• 13. R. Mikhov, V. Myasnichenko, S. Fidanova, L. Kirilov, and N. Sdobnyakov, “Influence of the temperature on simulated annealing method for metal nanoparticle structures optimization,” in Advanced Computing in Industrial Mathematics: 13th Annu. Meet. Bulg. Sect. of SIAM, Dec. 2018, Sofia, Bulgaria, Springer, to be published.
• 14. V. Myasnichenko, R. Mikhov, L. Kirilov, N. Sdobnykov, D. Sokolov, and S. Fidanova, “Simulation of diffusion processes in bimetallic nanofilms,” in Advanced Computing in Industrial Mathematics: 14th Annu. Meet. Bulg. Sect. of SIAM, Dec. 2019, Sofia, Bulgaria, Springer, submitted for publication.
• 15. V. M. Samsonov, N. Yu. Sdobnyakov, A. G. Bembel, D. N. Sokolov, and N. V. Novozhilov, “Size dependence of the melting temperature of metallic films: two possible scenarios,” J. Nano-Electron. Phys., vol. 5, no. 4, pp. 04005-1–04005-3, Dec. 2013.
• 16. V. M. Samsonov, N. Yu. Sdobnyakov, A. G. Bembel, D. N. Sokolov, and N. V. Novozhilov, “Thermodynamic approach to the size dependence of the melting temperatures of films,” Bull. Russ. Acad. Sci. Phys., vol. 78, no. 8, pp. 733–736, Sept. 2014.

Uwagi

1. Track 1: Artificial Intelligence

2. Technical Session: 13th International Workshop on Computational Optimization

3. Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).

Identyfikatory

DOI

10.15439/2020F135