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Citation: H. Mori, and T. Ozaki (2020), "Neural network atomic potential to investigate the dislocation dynamics in bcc iron", Physical Review Materials, 4(4), 040601. DOI: 10.1103/physrevmaterials.4.040601.
Abstract: To design the mechanical strength of body-centered-cubic (bcc) iron, clarifying the dislocation dynamics is very important. Using systematically constructed reference data based on density functional theory (DFT) calculations, we construct an atomic artificial neural network (ANN) potential to investigate the dislocation dynamics in bcc iron with the accuracy of DFT calculations. The bulk properties and defect formation energies predicted by the constructed ANN potential are in good agreement with the reference DFT calculations. The a0/2⟨111⟩110 screw dislocation core structure predicted by the ANN potential is compact and nondegenerate. The Peierls barrier predicted by the ANN potential is 35.3 meV per length of the Burgers vector. These results are consistent with the DFT results. Furthermore, not only the Peierls barrier, but also the two-dimensional energy profile of the screw dislocation core position predicted by the ANN potential are in excellent agreement with the DFT results. These results clearly demonstrate the reproducibility and transferability of the constructed ANN potential for investigating dislocation dynamics with the accuracy of the DFT. Combined with advanced atomistic techniques, the ANN potential will be highly useful for investigating the dislocation dynamics in bcc iron at finite temperatures.

LAMMPS pair_style aenet (custom) (2020--Mori-H--Fe--LAMMPS--ipr1)
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Notes: These files were sent by Hideki Mori (College of Industrial Technology, Japan) on 13 July 2020 and posted with his permission. This package provides the parameter file of the artificial neural network (ANN) potential for BCC iron, LAMMPS module for the ANN potential and the patch of aenet for the LAMMPS library. See the included readme file for instructions on installing aenet and incorporating it with LAMMPS. The included pair style patch is also available at https://github.com/HidekiMori-CIT/aenet-lammps.
Date Created: October 5, 2010 | Last updated: June 09, 2022