Development of interatomic potential for Al-Tb alloys using a deep neural network learning method

L Tang and ZJ Yang and TQ Wen and KM Ho and MJ Kramer and CZ Wang, PHYSICAL CHEMISTRY CHEMICAL PHYSICS, 22, 18467-18479 (2020).

DOI: 10.1039/d0cp01689f

An interatomic potential for the Al-Tb alloy around the composition of Al(90)Tb(10)is developed using the deep neural network (DNN) learning method. The atomic configurations and the corresponding total potential energies and forces on each atom obtained fromab initiomolecular dynamics (AIMD) simulations are collected to train a DNN model to construct the interatomic potential for the Al-Tb alloy. We show that the obtained DNN model can well reproduce the energies and forces calculated by AIMD simulations. Molecular dynamics (MD) simulations using the DNN interatomic potential also accurately describe the structural properties of the Al(90)Tb(10)liquid, such as partial pair correlation functions (PPCFs) and bond angle distributions, in comparison with the results from AIMD simulations. Furthermore, the developed DNN interatomic potential predicts the formation energies of the crystalline phases of the Al-Tb system with an accuracy comparable toab initiocalculations. The structure factors of the Al(90)Tb(10)metallic liquid and glass obtained by MD simulations using the developed DNN interatomic potential are also in good agreement with the experimental X-ray diffraction data. The development of short-range order (SRO) in the Al(90)Tb(10)liquid and the undercooled liquid is also analyzed and three dominant SROs,i.e., Al-centered distorted icosahedron (DISICO) and Tb-centered '3661' and '15551' clusters, respectively, are identified.

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