Exploring the Relationship between Composition and Li-Ion Conductivity in the Amorphous Li–La–Zr–O System
Source abstract
Abstract
Amorphous Li–La–Zr–O (a-LLZO), a promising candidate for solid electrolytes in all-solid-state Li batteries, demonstrates exceptional dendrite-inhibiting properties. By adjustment of its elemental composition, the Li-ion conductivity of a-LLZO can be modulated. In this study, we developed an interatomic potential function based on machine learning to describe amorphous LLZO systems with varying compositions. The relationship between element ratios and Li-ion conductivity is investigated by utilizing molecular dynamics simulations covering a wide range of spatial and temporal scales. Our results indicate that the degree of aggregation between La–La atoms is more severe than that between Zr–Zr atoms. Maintaining a constant Li content while increasing the level of Zr and reducing the level of La in the amorphous structure can create additional vacancies and promote Li ion diffusion.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Advancements in Battery Materials · Advanced Battery Materials and Technologies · Inorganic Chemistry and Materials
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Physics, OSED, Key Laboratory of Low Dimensional Condensed Matter Physics (Department of Education of Fujian Province), Xiamen University, Xiamen 361005, China
Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, Xiamen University, Xiamen 361005, China
Funding
National Natural Science Foundation of China
References (41)
Show source-provided references
1. 10.1039/C4RA02096K10.1039/C4RA02096K ↗
2. 10.1002/adfm.20190095010.1002/adfm.201900950 ↗
3. 10.1021/acs.chemrev.9b0026810.1021/acs.chemrev.9b00268 ↗
4. 10.1002/adfm.20040004410.1002/adfm.200400044 ↗
5. 10.1016/j.jpowsour.2018.04.01910.1016/j.jpowsour.2018.04.019 ↗
6. 10.1016/j.pmatsci.2017.04.00710.1016/j.pmatsci.2017.04.007 ↗
7. 10.1021/acs.jpcc.8b1069410.1021/acs.jpcc.8b10694 ↗
8. 10.1016/j.electacta.2016.12.01810.1016/j.electacta.2016.12.018 ↗
9. 10.1016/j.ssi.2013.09.00510.1016/j.ssi.2013.09.005 ↗
10. 10.1021/acsami.2c0136110.1021/acsami.2c01361 ↗
11. 10.1021/acsaem.0c0034710.1021/acsaem.0c00347 ↗
12. 10.1016/j.ssi.2012.09.01110.1016/j.ssi.2012.09.011 ↗
13. 10.1038/s43246-021-00177-410.1038/s43246-021-00177-4 ↗
14. 10.1002/admi.20200176710.1002/admi.202001767 ↗
15. 10.1103/PhysRevLett.98.14640110.1103/PhysRevLett.98.146401 ↗
16. 10.1103/PhysRevLett.104.13640310.1103/PhysRevLett.104.136403 ↗
17. 10.1126/sciadv.160301510.1126/sciadv.1603015 ↗
18. 10.1103/PhysRevLett.120.14300110.1103/PhysRevLett.120.143001 ↗
19. 10.1016/j.cpc.2018.03.01610.1016/j.cpc.2018.03.016 ↗
20. 10.1021/acs.jpclett.8b0302610.1021/acs.jpclett.8b03026 ↗
21. 10.1103/PhysRevLett.126.23600110.1103/PhysRevLett.126.236001 ↗
22. 10.1039/C9SC05116C10.1039/C9SC05116C ↗
23. 10.1039/D0CP01689F10.1039/D0CP01689F ↗
24. 10.1103/PhysRevB.47.55810.1103/PhysRevB.47.558 ↗
25. 10.1103/PhysRevB.54.1116910.1103/PhysRevB.54.11169 ↗
26. 10.1103/PhysRevB.50.1795310.1103/PhysRevB.50.17953 ↗
27. 10.1103/PhysRevLett.77.386510.1103/PhysRevLett.77.3865 ↗
28. Kingma, D. P.; Ba, J. Adam: A Method for Stochastic Optimization. arXiv 2014,10.48550/arXiv.1412.6980.10.48550/arXiv.1412.6980 ↗
29. 10.1063/1.481232310.1063/1.4812323 ↗
30. 10.1038/s41524-021-00595-510.1038/s41524-021-00595-5 ↗
31. 10.1002/wics.10110.1002/wics.101 ↗
32. 10.1103/PhysRevB.79.15520610.1103/PhysRevB.79.155206 ↗
33. 10.1039/C4TA02289K10.1039/C4TA02289K ↗
34. 10.1016/j.jpowsour.2014.09.16410.1016/j.jpowsour.2014.09.164 ↗
35. 10.1016/j.tsf.2014.11.01910.1016/j.tsf.2014.11.019 ↗
36. 10.1039/C6NR04162K10.1039/C6NR04162K ↗
37. 10.1149/2.0201701jes10.1149/2.0201701jes ↗
38. 10.1002/admi.20200042510.1002/admi.202000425 ↗
39. 10.1021/acsaem.1c0171010.1021/acsaem.1c01710 ↗
40. 10.1016/j.ssi.2022.11593810.1016/j.ssi.2022.115938 ↗
41. 10.1016/j.jnoncrysol.2008.12.02710.1016/j.jnoncrysol.2008.12.027 ↗