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

2025/09/03 by Hannes Gustafsson, Fabian Schwarz, Thijs Smolders +2 · 1 voice
Chemistry · Engineering · #Inorganic Fluorides and Related Compounds #Advancements in Battery Materials #Advanced Battery Materials and Technologies

paper · pdf · doi:10.1021/acs.jctc.5c00891

openalex publication_date 2025/09/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/31

Abstract

We present a method for large-scale DFT-based screening of ion diffusion in crystalline solids. This is accomplished by extending the Ionic TuTraSt method to sample the potential energy surface by using single-point DFT calculations. To drastically reduce the number of single-point DFT calculations, symmetry, interpolation, and exclusion of high-energy regions are employed. This approach is tested on a large data set of solid-state Li-ion conductors, for which the interpolation and high-energy exclusion are optimized to balance computational efficiency and accuracy of the obtained diffusion properties. Furthermore, the developed workflow is validated by comparison with ab initio molecular dynamics (AIMD) simulations on a set of known Li-ion superconducting materials.

Citations

• Effect of the damping function in dispersion corrected density functional theory
• CP2K: An electronic structure and molecular dynamics software package - Quickstep: Efficient and accurate electronic structure calculations
• Understanding Mobile Particles in Solid-State Materials: From the Perspective of Potential Energy Surfaces
• A consistent and accurateab initioparametrization of density functional dispersion correction (DFT-D) for the 94 elements H-Pu
• Separable dual-space Gaussian pseudopotentials

Discussions

• Hannes' manuscript about our methodology to study ion diffusion in crystalline solids has just been published. The interaction of the ions with the framework can in principle be calculated at any leve [bsky, 1 points, 0 comments]

Related

• Flow Matching for Accelerated Simulation of Atomic Transport in Crystalline Materials
• T-MSD: An improved method for ionic diffusion coefficient calculation from molecular dynamics
• Direct calculation of the ionic mobility in superionic conductors
• Universal Machine Learning Interatomic Potentials are Ready for Solid Ion Conductors
• Accurate Description of Ion Migration in Solid-State Ion Conductors from Machine-Learning Molecular Dynamics