Automatic diffusion path exploration for multivalent battery cathodes using geometrical descriptors

TL;DR

This paper introduces a geometrical descriptor-based method for automatic exploration of diffusion paths in multivalent battery cathodes, enabling efficient identification of transition states and diffusion characteristics in magnesium materials.

Contribution

It presents a Voronoi tessellation-based path finder algorithm combined with PCA for systematic diffusion path analysis using DFT data.

Findings

Automated transition state estimation prior to NEB calculations.

Effective clustering of diffusion paths using geometrical descriptors.

Method applicable to new materials beyond the studied set.

Abstract

Stable and fast ionic conductors for magnesium cathode materials have the prospect of enabling high energy density batteries beyond current Lithium-ion technologies. So far, only a few candidate materials have been identified leading to data only being scarcely available to the community. Here, we present a systematic study, in the framework of Density Functional Theory, including the estimation of the diffusion barrier for 16 materials through employing Nudged Elastic Band (NEB) calculations. By introducing a path finder algorithm based on the idea of Voronoi tessellations, we show that an estimate of the transition state configuration can be extracted automatically prior to running NEB-calculations. Using geometrical descriptors in combination with a principal component analysis it is possible to further sub-group the diffusion paths. This approach also extends to materials which are not part of the study, making it a viable approach to more efficiently explore crystal structures with distinguishable diffusion characteristics.