Highly efficient evaluation of diffusion networks in Li ionic conductors using a 3D-corrugation descriptor

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.