Data-driven prediction of complex crystal structures of dense lithium

Xiaoyang Wang, Zhenyu Wang, Pengyue Gao, Chengqian Zhang, Jian Lv (), Han Wang (), Haifeng Liu, Yanchao Wang and Yanming Ma ()
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Xiaoyang Wang: Jilin University
Zhenyu Wang: Jilin University
Pengyue Gao: Jilin University
Chengqian Zhang: DP Technology
Jian Lv: Jilin University
Han Wang: Institute of Applied Physics and Computational Mathematics
Haifeng Liu: Institute of Applied Physics and Computational Mathematics
Yanchao Wang: Jilin University
Yanming Ma: Jilin University

Nature Communications, 2023, vol. 14, issue 1, 1-6

Abstract: 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.

Date: 2023
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DOI: 10.1038/s41467-023-38650-y

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