Deep learning assisted exploration of superionic states and melting temperatures in Li-Sn superconductors

The unique spatial arrangement of Li atoms in Li-rich compounds induces properties on Li-rich compounds, including interstitial quasiatoms, superconductivity, and superionic states. However, the dependence of increasing Li concentration on electron-phonon coupling (EPC) and superionic behavior at high pressure remains inadequately understood. Here, we systematically investigate the crystal structure of the $\mathrm{L}{\mathrm{i}}_{x}\mathrm{Sn}$ ($x$ = 1--8) compound at 0--150 GPa using structure prediction and first-principles calculations. Band calculations reveal that the $P6/mmm$ phases of $\mathrm{L}{\mathrm{i}}_{x}\mathrm{Sn}$ ($x$ = 1--7) commonly exhibits Dirac points, van Hove singularities, and flat bands. Superconducting critical temperatures (${T}_{\mathrm{c}}$) were also investigated, with LiSn exhibiting a ${T}_{\mathrm{c}}$ of 9.3 K at 0 GPa. We further analyzed the relationship between superconductivity and EPC strength, phonon softening, linewidth, and density of states. Deep learning molecular dynamics simulations of LiSn, ${\mathrm{Li}}_{2}\mathrm{Sn}, {\mathrm{Li}}_{3}\mathrm{Sn}$, and ${\mathrm{Li}}_{4}\mathrm{Sn}$ demonstrate that when the Li concentration exceeds twice that of Sn, Li atoms undergo melting while Sn remains solid, indicating the emergence of a superionic state. Among them, ${\mathrm{Li}}_{3}\mathrm{Sn}$ exhibits the highest melting temperature of 1013 K. These findings enrich the crystal structure of Li-Sn compounds and provide a theoretical basis for constructing superconductors with a Kagome lattice and superionic states.

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Publication Details

Journal
Physical review. B./Physical review. B
Published
2025-08-28
DOI
https://doi.org/10.1103/g9dv-fl6q
Citations
1
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
0.52

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article

Deep learning assisted exploration of superionic states and melting temperatures in Li-Sn superconductors

Mengxin Yang, Defang Duan, Bohan Cao, Fubo Tian et al.
1 citations
Physical review. B./Physical review. B
Machine Learning in Materials Science
0.52
article

Deep learning assisted exploration of superionic states and melting temperatures in Li-Sn superconductors

Mengxin Yang, Defang Duan, Bohan Cao, Fubo Tian, Tian Cui, Xiaohan Wang, Liang Li, Haobo Li, Xinwei Wang
article en
1 citations

Abstract

The unique spatial arrangement of Li atoms in Li-rich compounds induces properties on Li-rich compounds, including interstitial quasiatoms, superconductivity, and superionic states. However, the dependence of increasing Li concentration on electron-phonon coupling (EPC) and superionic behavior at high pressure remains inadequately understood. Here, we systematically investigate the crystal structure of the $\mathrm{L}{\mathrm{i}}_{x}\mathrm{Sn}$ ($x$ = 1--8) compound at 0--150 GPa using structure prediction and first-principles calculations. Band calculations reveal that the $P6/mmm$ phases of $\mathrm{L}{\mathrm{i}}_{x}\mathrm{Sn}$ ($x$ = 1--7) commonly exhibits Dirac points, van Hove singularities, and flat bands. Superconducting critical temperatures (${T}_{\mathrm{c}}$) were also investigated, with LiSn exhibiting a ${T}_{\mathrm{c}}$ of 9.3 K at 0 GPa. We further analyzed the relationship between superconductivity and EPC strength, phonon softening, linewidth, and density of states. Deep learning molecular dynamics simulations of LiSn, ${\mathrm{Li}}_{2}\mathrm{Sn}, {\mathrm{Li}}_{3}\mathrm{Sn}$, and ${\mathrm{Li}}_{4}\mathrm{Sn}$ demonstrate that when the Li concentration exceeds twice that of Sn, Li atoms undergo melting while Sn remains solid, indicating the emergence of a superionic state. Among them, ${\mathrm{Li}}_{3}\mathrm{Sn}$ exhibits the highest melting temperature of 1013 K. These findings enrich the crystal structure of Li-Sn compounds and provide a theoretical basis for constructing superconductors with a Kagome lattice and superionic states.

Physical review. B./Physical review. BVol. 112(6)
Ningbo University (CN), Jilin University (CN), Shenyang Normal University (CN), Jilin Jianzhu University (CN), Jilin Medical University (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 34%
Machine Learning in Materials Science
0.52
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