Automatic Migration Path Exploration for Multivalent Battery Cathodes using Geometrical Descriptors

被引:16
作者
Bolle, Felix T. [1 ]
Bhowmik, Arghya [1 ]
Vegge, Tejs [1 ]
Maria Garcia Lastra, Juan [1 ]
Castelli, Ivano E. [1 ]
机构
[1] Tech Univ Denmark, Dept Energy Convers & Storage, Anker Engelundsvej 411, DK-2800 Lyngby, Denmark
关键词
density functional calculations; electrochemistry; geometrical descriptors; machine learning; migration barriers; LI; CONDUCTIVITY; PRINCIPLES; DIFFUSION; MOBILITY; IONS;
D O I
10.1002/batt.202100086
中图分类号
O646 [电化学、电解、磁化学];
学科分类号
081704 ;
摘要
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.
引用
收藏
页码:1516 / 1524
页数:9
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