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