Computationally Efficient DFT-Based Sampling of Ion Diffusion in Crystalline Solids.

Area of Science:

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

Background:

• Accurate prediction of ion diffusion is crucial for developing advanced energy storage materials.
• Traditional methods for calculating ion diffusion barriers are computationally expensive, limiting large-scale screening.
• Density Functional Theory (DFT) offers a powerful framework for electronic structure calculations.

Purpose of the Study:

• To present a computationally efficient method for large-scale screening of ion diffusion in crystalline solids.
• To extend the Ionic TuTraSt method for enhanced potential energy surface sampling.
• To optimize the balance between computational cost and accuracy for diffusion property prediction.

Main Methods:

• Extending the Ionic TuTraSt method using single-point DFT calculations.
• Employing symmetry, interpolation, and high-energy region exclusion to reduce DFT calculations.
• Optimizing interpolation and high-energy exclusion for solid-state Li-ion conductors.
• Validating the workflow against ab initio molecular dynamics (AIMD) simulations.

Main Results:

• A significant reduction in the number of required DFT calculations for ion diffusion screening.
• Accurate prediction of diffusion properties for Li-ion conductors.
• Demonstrated efficiency and accuracy of the developed workflow on a large dataset.

Conclusions:

• The presented method enables large-scale, accurate, and efficient screening of ion diffusion in crystalline materials.
• This approach is particularly valuable for accelerating the discovery of new solid-state Li-ion conductors.
• The workflow provides a robust tool for materials scientists and researchers in energy storage.