We show that a deep-learning neural network potential (DP) based on density functional theory (DFT) calculations can well describe Cu–Zr materials, an example of a binary alloy system, that can coexist in as ordered intermetallic and as an amorphous phase. The complex phase diagram for Cu–Zr makes it a challenging system for traditional atomistic force-fields that cannot accurately describe the different properties and phases. Instead, we show that a DP approach using a large database with ∼300k configurations can render results generally on par with DFT. The training set includes configurations of pristine and bulk elementary metals and intermetallic structures in the liquid and solid phases in addition to slab and amorphous configurations. The DP model was validated by comparing bulk properties such as lattice constants, elastic constants, bulk moduli, phonon spectra, and surface energies to DFT values for identical structures. Furthermore, we contrast the DP results with values obtained using well-established two embedded atom method potentials. Overall, our DP potential provides near DFT accuracy for the different Cu–Zr phases but with a fraction of its computational cost, thus enabling accurate computations of realistic atomistic models, especially for the amorphous phase.
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21 April 2020
Research Article|
April 16 2020
Optimization and validation of a deep learning CuZr atomistic potential: Robust applications for crystalline and amorphous phases with near-DFT accuracy
Christopher M. Andolina
;
Christopher M. Andolina
Department of Mechanical Engineering and Materials Science, University of Pittsburgh
, Pittsburgh, Pennsylvania 15216, USA
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Philip Williamson;
Philip Williamson
Department of Mechanical Engineering and Materials Science, University of Pittsburgh
, Pittsburgh, Pennsylvania 15216, USA
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Wissam A. Saidi
Wissam A. Saidi
a)
Department of Mechanical Engineering and Materials Science, University of Pittsburgh
, Pittsburgh, Pennsylvania 15216, USA
a)Author to whom correspondence should be addressed: alsaidi@pitt.edu
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a)Author to whom correspondence should be addressed: alsaidi@pitt.edu
J. Chem. Phys. 152, 154701 (2020)
Article history
Received:
February 19 2020
Accepted:
March 23 2020
Citation
Christopher M. Andolina, Philip Williamson, Wissam A. Saidi; Optimization and validation of a deep learning CuZr atomistic potential: Robust applications for crystalline and amorphous phases with near-DFT accuracy. J. Chem. Phys. 21 April 2020; 152 (15): 154701. https://doi.org/10.1063/5.0005347
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