Identification of stable Cu-Pd-Ag nanoparticles using neural network interatomic potentials

ORAL

Abstract

A neural network potential constructed with a stratified training scheme available in the MAISE package [1,2] has been used to find low-energy structures of elemental, binary and ternary Cu-Pd-Ag clusters. The efficiency of the employed unbiased global ground state evolutionary search for elemental nanoparticles was improved by co-evolving clusters across a range of sizes. We systematically compared the stability of the clusters found with the neural network model against previously reported structures found with the Gupta potential. Predictions made with the neural network show a consistent improvement in nanoparticle stability at the density functional theory level.

[1] https://github.com/maise-guide/maise
[2] S. Hajinazar, J. Shao, and A. N. Kolmogorov, Phys. Rev. B 95, 014114 (2017)

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Presenters

  • Samad Hajinazar

    Binghamton University, Physics, Applied Physics and Astronomy, Binghamton University

Authors

  • Samad Hajinazar

    Binghamton University, Physics, Applied Physics and Astronomy, Binghamton University

  • Ernesto D. Sandoval

    Binghamton University, Physics, Applied Physics and Astronomy, Binghamton University

  • Aiden J. Cullo

    Binghamton University

  • Aleksey Kolmogorov

    Binghamton University, Department of Physics, Applied Physics and Astronomy, Binghamton University, Physics, Applied Physics and Astronomy, Binghamton University