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Size effect in molecular dynamics simulation of nucleation process during solidification of pure metals: investigating modified embedded atom method interatomic potentials
A. Mahata, M. Asle Zaeem. Size effect in molecular dynamics simulation of nucleation process during solidification of pure metals: investigating modified embedded atom method interatomic potentials. Modelling and Simulation in Materials Science and Engineering 27 (2019) 085015 (17 pages).
Erratum: Size effect in molecular dynamics simulation of nucleation process during solidification of pure metals: investigating modified embedded atom method interatomic potentials (2019 Modelling Simul. Mater. Sci. Eng. 27 085015)
Due to the significant increase in computing power in recent years, the simulation size of atomistic methods for studying the nucleation process during solidification has been gradually increased, even to billion atom simulations (sub-micron length scale). But the question is how big of a model is required for size-independent and accurate simulations of the nucleation process during solidification? In this work, molecular dynamics simulations with model sizes ranging from ~2000 to ~8 million atoms were used to study nucleation during solidification. To draw general conclusions independent of crystal structures, the most advanced second nearest-neighbor modified embedded atom method interatomic potentials for Al (face-centered cubic), Fe (body-centered cubic), and Mg (hexagonal-close packed) were utilized for molecular dynamics simulations. We have analyzed several quantitative characteristics such as nucleation time, density of nuclei, nucleation rate, self-diffusion coefficient, and change in free energy during solidification. The results showed that by increasing the model size to about two million atoms, the simulations and measurable quantities become entirely independent of simulation cell size. The prediction of cell size required for size-independent computed data can considerably reduce the computational costs of atomistic simulations and at the same time increase the accuracy and reliability of the computational data.
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