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

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 an additional stable Li3Sn 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,low-symmetry 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.