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.
Authors
- Mengxin Yang (ORCID: https://orcid.org/0009-0001-4013-8202)
- Defang Duan (ORCID: https://orcid.org/0000-0002-6878-1830)
- Bohan Cao (ORCID: https://orcid.org/0009-0001-2263-697X)
- Fubo Tian (ORCID: https://orcid.org/0000-0001-9352-4126)
- Tian Cui (ORCID: https://orcid.org/0000-0002-9664-848X)
- Xiaohan Wang (ORCID: https://orcid.org/0000-0003-3362-2669)
- Liang Li (ORCID: https://orcid.org/0000-0002-6739-5022)
- Haobo Li (ORCID: https://orcid.org/0000-0002-9448-6771)
- Xinwei Wang
Institutions
- Ningbo University (CN)
- Jilin University (CN)
- Shenyang Normal University (CN)
- Jilin Jianzhu University (CN)
- Jilin Medical University (CN)
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
Funders
- National Natural Science Foundation of China