  The miscibility of Au and Li exhibits a potential application as an adhesion
layer and electrode material in secondary batteries. Here, to explore alloying
properties, we constructed a neural network potential (NNP) of Au-Li binary
systems based on density functional theory (DFT) calculations. To accelerate
construction of NNPs, we proposed an efficient and inexpensive method of
structural dataset generation. The predictions by the constructed NNP on
lattice parameters and phonon properties agree well with those obtained by DFT
calculations. We also investigated the mixing energy of Au$_{1-x}$Li$_{x}$ with
fine composition grids, showing excellent agreement with DFT verifications. We
found the existence of various compositions with structures on and slightly
above the convex hull, which can explain the lack of consensus on the Au-Li
stable phases in previous studies. Moreover, we newly found
Au$_{0.469}$Li$_{0.531}$ as a stable phase, which has never been reported
elsewhere. Finally, we examined the alloying process starting from the phase
separated structure to the complete mixing phase. We found that when multiple
adjacent Au atoms dissolved into Li, the alloying of the entire Au/Li interface
started from the dissolved region. This paper demonstrates the applicability of
NNPs toward miscible phases and provides the understanding of the alloying
mechanism.
