Development of Interatomic Potential for Al-Tb Alloy by Deep Learning Method release_okoywvn2o5defexnavtucl5x44

by L. Tang, Z. J. Yang, T. Q. Wen, K. M. Ho, M. J. Kramer, C. Z. Wang

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2020  

Abstract

An interatomic potential for Al-Tb alloy around the composition of Al90Tb10 was developed using the deep learning method with DeePMD-kit package. The atomic configurations and the corresponding total potential energies and forces on each atom obtained in the ab initio molecular dynamics (AIMD) simulations are collected to train a deep neural network model to construct the interatomic potential for Al-Tb alloy. We show the obtained deep neural network model can well reproduce the energies and forces calculated by AIMD. MD simulations using the neural network interatomic potential also describe the structural properties of Al90Tb10 liquid well, such as the partial pair correlation functions and the bond angle distribution, in comparison with the results from AIMD. Furthermore, the developed neural network interatomic potential predicts the formation energies of crystalline phases of Al-Tb system with the same accuracy as ab initio calculations. The structure factor of Al90Tb10 metallic glass obtained by MD simulation using the developed neural network interatomic potential is also in good agreement with the experimental X-ray diffraction data.
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Date   2020-01-19
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