Prediction of stable Li-Sn compounds: boosting ab initio searches with neural network potentials

TL;DR

This study combines machine learning and ab initio methods to efficiently predict stable Li-Sn compounds, discovering new phases at ambient and high pressures, demonstrating the potential of neural network potentials in complex materials discovery.

Contribution

It introduces a novel approach using neural network potentials to accelerate ab initio searches, uncovering previously overlooked stable Li-Sn alloys and phases under different pressures.

Findings

Discovered a new stable Li3Sn phase with a BCC-based hR48 structure.

Predicted a high-T LiSn4 ground state.

Identified new phases at 20 GPa that destabilize previous models.

Abstract

The Li-Sn binary system has been the focus of extensive research because it features Li-rich alloys with potential applications as battery anodes. Our present re-examination of the binary system with a combination of machine learning and ab initio methods has allowed us to screen a vast configuration space and uncover a number of overlooked thermodynamically stable alloys. At ambient pressure, our evolutionary searches identified a new stable Li$_3$Sn phase with a large BCC-based hR48 structure and a possible high-T LiSn$_4$ ground state. By building a simple model for the observed and predicted Li-Sn BCC alloys we constructed an even larger viable hR75 structure at an exotic 19:6 stoichiometry. At 20 GPa, new 11:2, 5:1, and 9:2 phases found with our global searches destabilize previously proposed phases with high Li content. The findings showcase the appreciable promise machine learning interatomic potentials hold for accelerating ab initio prediction of complex materials.