晶体结构预测
晶体结构
相图
锂(药物)
Crystal(编程语言)
材料科学
化学物理
相(物质)
结晶学
计算机科学
化学
医学
内分泌学
有机化学
程序设计语言
作者
Xiaoyang Wang,Zhenyu Wang,Pengyue Gao,Chengqian Zhang,Jian Lv,Han Wang,Haifeng Liu,Yanchao Wang,Yanming Ma
标识
DOI:10.1038/s41467-023-38650-y
摘要
Abstract 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.
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