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Li diffusion in oxygen-chlorine mixed anion borosilicate glasses using a machine-learning simulation.

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Area of Science:

  • Materials Science
  • Solid-State Chemistry
  • Computational Materials Science

Background:

  • Lithium borate glasses are promising interfacial materials for solid-state batteries due to their formability.
  • Chlorine incorporation is known to enhance electron conductivity in borate glasses.

Purpose of the Study:

  • To investigate the impact of chlorine doping on lithium dynamics in borate and borosilicate glasses.
  • To validate the accuracy of machine-learning interatomic potentials (MLIPs) for modeling these glass systems.

Main Methods:

  • Molecular dynamics (MD) simulations utilizing a machine-learning interatomic potential (MLIP).
  • Verification of MLIP accuracy against experimental data including density, neutron diffraction S(q), and glass transition temperatures (Tg).
  • Analysis of structural properties and lithium ion diffusion mechanisms.

Main Results:

  • MLIP-MD simulations showed increased lithium ion diffusion with higher chlorine content in both lithium borate (LBCl) and borosilicate (LBSCl) glasses.
  • Structural analysis revealed chlorine primarily interacts with lithium ions, enhancing their mobility.
  • While density predictions required NVT-then-NPT ensemble relaxation, the models showed reasonable accuracy.

Conclusions:

  • MLIPs are suitable for modeling chlorine-containing borate glasses and investigating chlorine's effect on ionic conductivity.
  • Increased chlorine content moderately enhances lithium ion mobility, beneficial for battery applications.
  • Further refinement of MLIPs is needed to accurately capture the middle-range structure (S(q)) of these glasses.