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 LiSn₄ 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.

Li-Sn 二元系统一直是广泛研究的焦点，因为它具有富锂合金，具有作为电池阳极的潜在应用。我们目前结合机器学习和从头算方法对二元系统的重新检查使我们能够筛选出巨大的配置空间并发现许多被忽视的热力学稳定合金。在环境压力下，我们的进化搜索确定了一个额外的稳定 Li ₃ Sn 相，它具有大的基于 BCC 的 hR48 结构和可能的高T LiSn ₄基态。通过为观察和预测的 Li-Sn BCC 合金建立一个简单的模型，我们以奇异的 19:6 化学计量构建了一个更大的可行 hR75 结构。在 20 GPa 时，通过我们的全局搜索发现的低对称 11:2、5:1 和 9:2 相会破坏之前提出的高锂含量相的稳定性。这些发现展示了机器学习原子间潜力对于加速复杂材料的从头预测的可观前景。