Automatic migration path exploration for multivalent battery cathodes using geometrical descriptors

Felix Tim Bölle, Arghya Bhowmik, Tejs Vegge, Juan Maria García Lastra, Ivano Eligio Castelli*

*Corresponding author for this work

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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 migration 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 migration 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 migration characteristics.
Original languageEnglish
JournalBatteries and Supercaps
Issue number9
Pages (from-to)1516-1524
Number of pages9
Publication statusPublished - 2021


  • Density functional calculations
  • Electrochemistry
  • Geometrical descriptors
  • Machine learning
  • Migrations barriers


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