Skip to content
DraconiticResearch
Search Sources API
197,768 studies31 sourcesUpdated 18 min ago
Peer-reviewedCC BY-NC-NDnpj Computational Materials

Exploring lithium diffusion in LiF with machine learning potentials: from point defects to collective ring diffusion

  • Paolo De Angelis1,2,3,4
  • Umberto Raucci2,3
  • Francesco Mambretti2,3
  • Matteo Fasano1,4
  • Eliodoro Chiavazzo1,5,4
  • Pietro Asinari1,5,6,4
  • Michele Parrinello2,3
6 affiliations
  1. 1Department of Energy “Galileo Ferraris”, Politecnico di Torino, Turin, Italy
  2. 2Laboratory of Atomistic Simulations, Italian Institute of Technology, Genova, Italy
  3. 3Italian Institute of Technology
  4. 4Polytechnic University of Turin
  5. 5Istituto Nazionale di Ricerca Metrologica, Torino, Italy
  6. 6Istituto Nazionale di Ricerca Metrologica
Open at sourceDOICite

Abstract

Lithium fluoride (LiF) is a fundamental inorganic component of the solid electrolyte interphase (SEI) in lithium-ion batteries, yet its intrinsic ionic transport properties remain poorly understood, since there is a lack of direct experimental data, and standard computational approaches have difficulties in modeling these phenomena. Here, we train a machine learning based interatomic potential (MLIP) to perform large-scale Molecular Dynamics simulations of lithium diffusion in bulk LiF with near-DFT accuracy. Our study reveals not only conventional vacancy and interstitial transport mechanisms, but also a novel collective ring diffusion process involving six lithium ions. This mechanism arises under high interstitial concentrations, and it is stabilized by partial charge delocalization. Given the dynamic evolution of the SEI under electrochemical cycling, we suggest that such correlated diffusion events may be transiently active in LiF-rich regions. More broadly, our results demonstrate the power of MLIPs to access long timescales and complex dynamical behavior, providing a robust framework for future multiscale modeling of the SEI.

Related

  1. Reactive Machine-Learned Potentials Improve Predictions of Water Diffusion Behaviour in Defective Industrial Zeolite HY
    Indranil Saha, Alexander James Porter, Andreas Erlebach +4
    ChemRxivPreprintCC BYFull textInorganic & organometallic chemistryChemRxiv
  2. Coordination–Diffusion Coupling (CDC) in Dual-Anion Ionic-Liquid/Carbonate Electrolytes: Molecular Origins of Lithium-Ion Self-Diffusion
    Abraham Molla Wagaye, Abraham molla Wagaye
    ChemRxivPreprintCC BYGeneral chemistryChemRxiv
  3. Effect of lithium diffusion into Ga2O3 thin films
    A. Sacchi, M. Sidoli, A. Ardenghi +26
    Applied Surface SciencePeer-reviewedCC BY1 citedMaterials & polymersHAL
  4. Correcting the Liquid Densities of Universal Machine-Learning Interatomic Potentials Using Local-Number-Density Repulsion
    Shunsuke Mieda
    ChemRxivPreprintLicence not statedGeneral chemistryChemRxiv
  5. pdb2reaction: End-to-End Reaction-Path Elucidation from PDB Structures Using Machine-Learning Interatomic Potentials
    Takuto Ohmura, Hajime Sato, Tohru Terada
    ChemRxivPreprintLicence not statedGeneral chemistryChemRxiv

Cite

BibTeXRIS
BibTeX
@article{angelis2026exploring,
  title = {{Exploring lithium diffusion in LiF with machine learning potentials: from point defects to collective ring diffusion}},
  author = {Paolo De Angelis and Umberto Raucci and Francesco Mambretti and Matteo Fasano and Eliodoro Chiavazzo and Pietro Asinari and Michele Parrinello},
  journal = {npj Computational Materials},
  year = {2026},
  month = may,
  doi = {10.1038/s41524-026-02132-8},
  url = {https://doi.org/10.1038/s41524-026-02132-8},
  note = {CC BY-NC-ND},
}
RIS
TY  - JOUR
TI  - Exploring lithium diffusion in LiF with machine learning potentials: from point defects to collective ring diffusion
AU  - Paolo De Angelis
AU  - Umberto Raucci
AU  - Francesco Mambretti
AU  - Matteo Fasano
AU  - Eliodoro Chiavazzo
AU  - Pietro Asinari
AU  - Michele Parrinello
PY  - 2026
DA  - 2026/05/20
JO  - npj Computational Materials
DO  - 10.1038/s41524-026-02132-8
UR  - https://doi.org/10.1038/s41524-026-02132-8
AB  - Lithium fluoride (LiF) is a fundamental inorganic component of the solid electrolyte interphase (SEI) in lithium-ion batteries, yet its intrinsic ionic transport properties remain poorly understood, since there is a lack of direct experimental data, and standard computational approaches have difficulties in modeling these phenomena. Here, we train a machine learning based interatomic potential (MLIP) to perform large-scale Molecular Dynamics simulations of lithium diffusion in bulk LiF with near-DFT accuracy. Our study reveals not only conventional vacancy and interstitial transport mechanisms, but also a novel collective ring diffusion process involving six lithium ions. This mechanism arises under high interstitial concentrations, and it is stabilized by partial charge delocalization. Given the dynamic evolution of the SEI under electrochemical cycling, we suggest that such correlated diffusion events may be transiently active in LiF-rich regions. More broadly, our results demonstrate the power of MLIPs to access long timescales and complex dynamical behavior, providing a robust framework for future multiscale modeling of the SEI.
SN  - 2057-3960
ER  -