Abstract
Surface diffusion plays a crucial role in the formation of the shape and morphology of growing nanoparticles and nanofilms. Bulk heterodiffusion occurs at uneven (irregular) concentrations of several metals, in the presence of free energy in the system. Atoms of each sort tend to be evenly distributed in volume and form mixed bonds. In this paper, we propose an approach for modeling diffusion processes in nanoalloys by the vacancy mechanism. It is a hybrid Monte Carlo approach based on computing the probability for a transition of each atom belonging to the first three coordination spheres. The energy of the system is computed with a tight binding potential. The efficiency of the approach is demonstrated simulating Au–Ag bimetallic nanofilms with a different number of vacancies in the crystal lattice and a different temperature.
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Acknowledgements
This work was partially funded by Russian Federation of Basic Research, project number 20-37-70007, by the Ministry of Science and Higher Education of the Russian Federation in the framework of the State Program in the Field of the Research Activity, project number 0817-2020-0007. Stefka Fidanova was supported by the Bulgarian NSF under the grant DFNI-DN 12/5 and by the Grant No BG05M2OP001-1.001-0003, financed by the Science and Education for Smart Growth Operational Program and co-financed by the European Union through the European structural and Investment funds. LeoneedKirilov and Rossen Mikhov were supported by the National Scientific Program “Information and Communication Technologies for a Single Digital Market in Science, Education and Security (ICTinSES)”, Ministry of Education and Science—Bulgaria, and by the Grant No BG05M2OP001-1.001-0003, financed by the Science and Education for Smart Growth Operational Program and co-financed by the European Union through the European structural and Investment funds.
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Myasnichenko, V., Mikhov, R., Kirilov, L., Sdobnykov, N., Sokolov, D., Fidanova, S. (2022). Simulation of Diffusion Processes in Bimetallic Nanofilms. In: Fidanova, S. (eds) Recent Advances in Computational Optimization. WCO 2020. Studies in Computational Intelligence, vol 986. Springer, Cham. https://doi.org/10.1007/978-3-030-82397-9_11
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