Key Laboratory of Material Simulation Methods & Software of Ministry of Education and State Key Laboratory of Superhard Materials, College of Physics, Jilin University, 130012, Changchun, People's Republic of China.
Laboratory of Computational Physics, Institute of Applied Physics and Computational Mathematics, Fenghao East Road 2, 100094, Beijing, People's Republic of China.
Nat Commun. 2023 May 22;14(1):2924. doi: 10.1038/s41467-023-38650-y.
Lithium (Li) is a prototypical simple metal at ambient conditions, but exhibits remarkable changes in structural and electronic properties under compression. There has been intense debate about the structure of dense Li, and recent experiments offered fresh evidence for yet undetermined crystalline phases near the enigmatic melting minimum region in the pressure-temperature phase diagram of Li. Here, we report on an extensive exploration of the energy landscape of Li using an advanced crystal structure search method combined with a machine-learning approach, which greatly expands the scale of structure search, leading to the prediction of four complex Li crystal structures containing up to 192 atoms in the unit cell that are energetically competitive with known Li structures. These findings provide a viable solution to the observed yet unidentified crystalline phases of Li, and showcase the predictive power of the global structure search method for discovering complex crystal structures in conjunction with accurate machine learning potentials.
锂（Li）在常温常压下是一种典型的简单金属，但在压缩下其结构和电子性质会发生显著变化。关于致密 Li 的结构一直存在激烈的争论，最近的实验为 Li 的压力-温度相图中神秘的熔化最小区域附近尚未确定的结晶相提供了新的证据。在这里，我们使用先进的晶体结构搜索方法结合机器学习方法，对 Li 的能量景观进行了广泛的探索，这大大扩展了结构搜索的规模，导致了对四个复杂 Li 晶体结构的预测，这些结构在晶胞中包含多达 192 个原子，与已知的 Li 结构具有竞争力。这些发现为观察到的但未被识别的 Li 结晶相提供了可行的解决方案，并展示了全局结构搜索方法与准确的机器学习势结合发现复杂晶体结构的预测能力。
