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Structural optimization of Fe nanoclusters based on multi-populations differential evolution algorithm

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Abstract

Structural optimization of Fe nanoclusters is of importance to their applications since their magnetic and catalytic properties are strongly dependent on their structures. In this article, the lowest-energy structures of Fe nanoclusters have been investigated using a multi-populations differential evolution algorithm. Finnis–Sinclair potentials and Johnson potentials have been, respectively, used to describe the interatomic interactions. The algorithm performance has been first evaluated by comparing the results of Finnis–Sinclair potentials with Cambridge Cluster Database. Two lower-energy structures of Fe n clusters (N = 83 and 84) have been discovered. Our results show that many of the structural configurations optimized with the two potentials are different, indicating that the potential model plays an important role in structural optimization. However, the structures of Fe clusters exhibit the similar growth patterns as the cluster increasing for the two potentials, that is, their lowest-energy structures contain many icosahedra, and the number of the icosahedral rings increases with their sizes. Furthermore, the stability analysis by investigating the finite difference and the second finite difference of energy indicates that the two potentials predict the same relative stability at the magic numbers.

Graphical Abstract

The comparison of optimized structures of Fe n clusters at magic numbers (N = 13, 19, 23, 26, 29, 55), respectively, using Finnis–Sinclair and Johnson potentials.

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Notes

  1. http://www-wales.ch.cam.ac.uk/~wales/CCD/FS_Fe/iron_fs.html.

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Acknowledgments

This work is supported by the National Natural Science Foundation of China (Grant Nos. 11474234 and 51271156), the Specialized Research Fund for the Doctoral Program of Higher Education of China (Grant No. 20130121110012), and the Natural Science Foundation of Fujian Province of China (Grant No. 2013J01255).

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Authors and Affiliations

  1. Department of Automation, Xiamen University, Xiamen, 361005, China

    Tun-Dong Liu, Tian-E Fan, Ji-Wen Zheng & Gui-Fang Shao

  2. Department of Mechanical and Manufacturing Engineering, University of Calgary, Calgary, T2N 1N4, Canada

    Tun-Dong Liu & Qiao Sun

  3. Department of Physics, Xiamen University, Xiamen, 361005, China

    Yu-Hua Wen

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  1. Tun-Dong Liu
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  2. Tian-E Fan
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Correspondence to Gui-Fang Shao.

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Liu, TD., Fan, TE., Zheng, JW. et al. Structural optimization of Fe nanoclusters based on multi-populations differential evolution algorithm. J Nanopart Res 18, 77 (2016). https://doi.org/10.1007/s11051-016-3361-x

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