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Vacancy-enhanced Li solid-solution anodes for solid-state batteries: computational screening enabling high critical current density
Arseniy S. Burov, Nadezhda A. Ashikhmina, Nurlan Zhanabayev, Anton O. Boev, Aleksandra D. Radina, Ilya V. Chepkasov, Alexander G. Kvashnin, Dmitry A. Aksyonov

Vacancy-enhanced Li solid-solution anodes for solid-state batteries: computational screening enabling high critical current density

Computer Science & Mathematics Published 2026 Verification Pending

Overview & Synopsis

Sluggish vacancy-mediated self-diffusion in lithium metal limits the critical current density (CCD) of Li anodes in solid-state batteries by promoting interfacial void formation during stripping. To enhance the effective vacancy concentration while preserving the bcc Li host, we screen 50 substitutional solutes using density functional theory (DFT) within a funnel based on automatic phase diagram construction, solution energies, solute-vacancy binding energies, and experimental solubility limits. Using an adaptation of the Lomer model for the dilute limit, we show that only Ag, Ge, and Cd solid solutions are predicted to reach $1~\mathrm{mA~cm^{-2}}$ within their solubility limits. The thermodynamically estimated CCD of Ag at $\sim 9$ at.% reaches $\sim 4~\mathrm{mA~cm^{-2}}$ at a theoretical capacity of $1522~\mathrm{mAh~g^{-1}}$. Migration barrier calculations indicate that the enhancement may be partially offset by vacancy trapping, necessitating further validation via molecular dynamics simulations. Notably, Hume-Rothery rules alone prove insufficient as prefilters, whereas machine-learning models trained on the DFT data can eliminate most unsuitable solutes early in the funnel and extend the workflow to related solid-solution searches.

Indexed from repository: arXiv (arxiv.org)
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Document Information

Document Title Vacancy-enhanced Li solid-solution anodes for solid-state batteries: computational screening enabling high critical current density
Author / Organization Arseniy S. Burov, Nadezhda A. Ashikhmina, Nurlan Zhanabayev, Anton O. Boev, Aleksandra D. Radina, Ilya V. Chepkasov, Alexander G. Kvashnin, Dmitry A. Aksyonov
Subject & Discipline cond-mat.mtrl-sci
Categories Computer Science & Mathematics
Publication Year 2026
License / Rights arXiv Open Access / Creative Commons
Resource Verification Listed PDF (Verification Pending)
Original Host Website arXiv (arxiv.org)
File Format application/pdf (Portable Document Format)
Language EN
Catalog ID DOC-116654
Original Source Link Visit Source Page on arxiv.org »