Diffusion and Deformation Mechanism Maps in Li Metal from Atomistic Simulations
Source abstract
Abstract
The rate of Li transport in the Li metal anode is important for the operation of Li metal-solid state batteries. Here, transport rates due to diffusion and creep are predicted in Li using atomistic simulations. First, molecular dynamics is used to estimate the rate of Li diffusion along dislocations and in grain boundary triple junctions. By combining this data with that from a prior study of grain boundary diffusion the dominant mechanisms and rates of self-diffusion in Li polycrystals are predicted as a function of grain size, grain shape, dislocation density, and temperature. Second, the dominant creep mechanisms are predicted and used to estimate critical current densities and void annihilation times. Grain boundary sliding and coble creep are the dominant mechanisms for micron-sized grains. Finally, a continuum model for interfacial contact loss reveals that high dislocation densities of ∼10 12 /cm 2 enable achieving battery performance targets for Li grain sizes of ∼10 μm.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Advancements in Battery Materials · Titanium Alloys Microstructure and Properties · Magnetic Properties of Alloys
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Mechanical Engineering, University of Michigan, 2350 Hayward Avenue, Ann Arbor, Michigan 48109, United States
Walker Department of Mechanical Engineering
University of Texas at Austin
Texas Materials Institute and Oden Institute for Computational Engineering and Sciences
Funding
Basic Energy Sciences
References (37)
Show source-provided references
1. 10.1016/j.matchemphys.2023.12879610.1016/j.matchemphys.2023.128796 ↗
2. 10.1016/j.jpowsour.2022.23194310.1016/j.jpowsour.2022.231943 ↗
3. 10.3390/engproc202509003910.3390/engproc2025090039 ↗
4. 10.1021/acsami.8b1978710.1021/acsami.8b19787 ↗
5. 10.1038/s41560-017-0047-210.1038/s41560-017-0047-2 ↗
6. 10.1038/s41563-024-02055-z10.1038/s41563-024-02055-z ↗
7. 10.1038/s41563-019-0438-910.1038/s41563-019-0438-9 ↗
8. 10.1016/j.electacta.2016.12.01810.1016/j.electacta.2016.12.018 ↗
9. 10.1002/aenm.20170100310.1002/aenm.201701003 ↗
10. 10.1002/aenm.20190256810.1002/aenm.201902568 ↗
11. 10.1021/acsami.9b0253710.1021/acsami.9b02537 ↗
12. 10.1016/j.jpowsour.2020.22866610.1016/j.jpowsour.2020.228666 ↗
13. 10.1149/1.211979510.1149/1.2119795 ↗
14. 10.1016/j.matt.2023.02.00810.1016/j.matt.2023.02.008 ↗
15. 10.1039/D3TA03814A10.1039/D3TA03814A ↗
16. 10.1016/j.joule.2019.06.01710.1016/j.joule.2019.06.017 ↗
17. 10.1016/j.actamat.2021.11707610.1016/j.actamat.2021.117076 ↗
18. 10.1007/s10853-018-2971-310.1007/s10853-018-2971-3 ↗
19. 10.1021/acsenergylett.4c0122910.1021/acsenergylett.4c01229 ↗
20. 10.1088/0305-4608/15/1/00910.1088/0305-4608/15/1/009 ↗
21. 10.1088/0965-0393/20/3/03500510.1088/0965-0393/20/3/035005 ↗
22. 10.1103/PhysRevB.79.17411010.1103/PhysRevB.79.174110 ↗
23. 10.1016/S0921-5093(00)01367-810.1016/S0921-5093(00)01367-8 ↗
24. 10.1002/adfm.20221106710.1002/adfm.202211067 ↗
25. 10.1590/1980-5373-mr-2017-051510.1590/1980-5373-mr-2017-0515 ↗
26. 10.1063/1.271182010.1063/1.2711820 ↗
27. 10.1038/s41563-024-02006-810.1038/s41563-024-02006-8 ↗
28. 10.1016/j.ssi.2005.12.00210.1016/j.ssi.2005.12.002 ↗
29. 10.1016/0025-5416(81)90100-210.1016/0025-5416(81)90100-2 ↗
30. 10.1016/j.joule.2022.09.00910.1016/j.joule.2022.09.009 ↗
31. 10.1149/2.0221902jes10.1149/2.0221902jes ↗
32. Integration and Optimization of Novel Ion Conducting Solids(IONICS) Program Overview ; ARPA, https://arpa-e.energy.gov/sites/default/files/migrated/documents/files/IONICS_Project%20Descriptions_FINAL.pdf.
33. 10.1149/2.0051513jes10.1149/2.0051513jes ↗
34. 10.1038/srep3426710.1038/srep34267 ↗
35. 10.1016/j.jallcom.2021.16238910.1016/j.jallcom.2021.162389 ↗
36. 10.1557/mrs.2020.14810.1557/mrs.2020.148 ↗
37. 10.26434/chemrxiv-2025-g0nbh-v210.26434/chemrxiv-2025-g0nbh-v2 ↗