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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.

The structure and dynamics of a material are essentially determined by the complex combination of potential energy landscapes experienced by the individual atoms in the system. In turn, valuable information on the properties of the material is encoded in the shapes of the potential energy landscape. For example, configurations of particles within a solid are determined by the shapes and presence of energetic basins, and the self-diffusion of mobile particles is defined by the geometry of how these energetic basins are connected to form paths. Understanding diffusion processes in solids at the atomistic scale is crucial for many important applications such as predicting Li-ion conduction through a solid-state battery cell or membranes for separation processes including carbon capture and water purification. While modeling can facilitate such understanding, there are still many challenges to overcome in terms of reaching relevant length and time scales that capture the complexity of the material. In this Perspective, we will discuss state-of-the-art modeling methods for mass transport inside a solid-state material and how they relate to the geometry of the potential energy landscape. We believe that approaching diffusion from a geometrical standpoint offers great promise in advancing modeling methodologies while yielding a better understanding of the structure-dynamic properties relationship and rate-limiting processes.