Investigation of the mechanical responses of copper nanowires based on molecular dynamics and coarse-grained molecular dynamics
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The mesoscale coarse-grained molecular dynamics (CG-MD) models for copper nanowires with different crystallographic orientations are developed via increasing the integration time step and grouping a certain number of atoms into one mesoscale particle. The tensile and torsional responses of copper nanowires at various temperatures and loading rates are then studied using the CG-MD and molecular dynamics (MD) simulations. In the tensile cases, the CG-MD simulations yield the tendency of Young’s modulus with a good agreement with that by the MD. For the torsional loading, the relation between loading rate and critical angle by the CG-MD is also in line with that by the MD, while the CG-MD predictions for low temperatures are not in close agreement with those by the MD. Although the CG-MD model could not perfectly fit the results from the MD and requires further improvement, it could be used as a starting point to evaluate the mechanical response of nanowire with less computational expenses than the MD model.
KeywordsMesoscale model Molecular dynamics Tension Torsion Metal nanowire Thermal effect
This work was supported in part by the National Natural Science Foundation of China (11672062 and 11232003), and National Center for High-Performance Computing, National Applied Research Laboratories (MOST 106-2221-E-492-014).
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Conflict of interest
The authors declare that they have no conflict of interest.
- 26.Gan Y, Jiang S, Su Y, Sewell TD, Chen Z (2016) A coarse-grained model for fcc metals based on hierarchical coupling between molecular dynamics and isothermal dissipative particle dynamics. Chin J Comput Mech 33:621Google Scholar
- 34.Qian LX (1983) Engineering structural optimization design. Hydraulic-Electric Press, BeijingGoogle Scholar
- 36.Goldberg DE (1989) Sizing populations for serial and parallel genetic algorithms. In Proceedings of 3rd international conference on genetic algorithmsGoogle Scholar