论文标题
稳定的LI-SN化合物的预测：通过神经网络潜力提高从头开始搜索
Prediction of stable Li-Sn compounds: boosting ab initio searches with neural network potentials
论文作者
论文摘要
LI-SN二进制系统一直是广泛研究的重点，因为它具有带有潜在应用的电池阳极的富合金。我们目前通过机器学习和从头算方法结合的二进制系统重新检查了二进制系统，使我们能够筛选庞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新的稳定的li $ _3 $ sn阶段，具有大型基于BCC的HR48结构和可能的High-T Lisn $ _4 $基础状态。通过为观察到的LI-SN BCC合金构建一个简单的模型，我们在异国情调的19：6化学计量学上构建了更大的可行HR75结构。在20 GPA时，新的11：2、5：1和9：2的阶段在我们的全球搜索中发现了先前提出的具有较高LI含量的阶段。这些发现展示了可观的有前途的机器学习间原子潜能，以加速复杂材料的依次预测。
The Li-Sn binary system has been the focus of extensive research because it features Li-rich alloys with potential applications as battery anodes. Our present re-examination of the binary system with a combination of machine learning and ab initio methods has allowed us to screen a vast configuration space and uncover a number of overlooked thermodynamically stable alloys. At ambient pressure, our evolutionary searches identified a new stable Li$_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.
