Machine learning as a tool to accelerate the search for new materials for metal-ion batteries

The search for new solid ionic conductors is an important topic of material science that requires significant resources, but can be accelerated using machine learning (ML) techniques. In this work, ML methods were applied to predict the migration energy of working ions. The training set is based on data on 225 lithium ion migration channels in 23 ion conductors. The descriptors were the parameters of free space in the crystal obtained by the Voronoi partitioning method. The accuracy of migration energy prediction was evaluated by comparison with the data obtained by the density functional theory method. Two methods of ML were applied in the work: support vector regression and ordinal regression. It is shown that the parameters of free space in a crystal correlate with the migration energy, while the best results are obtained by ordinal regression. The developed ML models can be used as an additional filter in the analysis of ionic conductivity in solids.