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Area of Science:

  • Materials Science
  • Computational Chemistry
  • Electrochemistry

Background:

  • Developing advanced materials for lithium-ion batteries is crucial for energy storage.
  • Accurate prediction of ion transport properties is essential for material screening.
  • Existing computational methods can be computationally expensive for large-scale screening.

Purpose of the Study:

  • To present a highly efficient computational approach for screening lithium (Li) ion conducting materials.
  • To demonstrate the approach's performance on olivine-type oxides and thiophosphates.
  • To establish a descriptor for automatic determination of Li ion diffusion pathways and conductivity correlation.

Main Methods:

  • Topological analysis of electrostatic (Coulomb) potential from density functional theory (DFT) calculations.
  • Augmentation with a Born-Mayer-type repulsive term between Li ions and anions.
  • 3D-corrugation descriptor for automatic diffusion pathway determination and migration barrier calculation.

Main Results:

  • The approach successfully screened Li ion conducting materials, including olivine-type oxides and thiophosphates.
  • Migration barriers calculated by the method closely match DFT nudged elastic band (NEB) results (within ~0.1 eV).
  • The 3D-corrugation descriptor shows a strong correlation with Li ion conductivity.

Conclusions:

  • The presented computational approach is highly efficient for screening Li ion conducting materials.
  • It provides an accurate and automated method for determining diffusion pathways and predicting conductivity.
  • This tool can accelerate the evaluation, ranking, and optimization of novel battery materials.