Phase stability of Au-Li binary systems studied using neural network potential
Koji Shimizu, Elvis F. Arguelles, Wenwen Li, Yasunobu Ando, Emi, Minamitani, and Satoshi Watanabe

TL;DR
This study develops a neural network potential for Au-Li systems to accurately predict phase stability, revealing new stable phases and elucidating alloying mechanisms relevant for battery applications.
Contribution
We constructed an efficient neural network potential for Au-Li systems based on DFT data, enabling detailed phase stability analysis and discovery of a new stable phase.
Findings
Identified various compositions near the convex hull, explaining previous phase stability discrepancies.
Discovered a new stable phase Au0.469Li0.531 not reported before.
Demonstrated alloying process starting from phase separation to complete mixing.
Abstract
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 AuLi 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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
