Finding Novel Fast Ionic Conductors Using Combined Techniques from Density Functional Theory and Materials Informatics

Computational new material search is an ongoing hot topic for research in various fields of applications. In here, we show our works related to efficient computational methods for finding novel fast ionic conductors for potential use in solid-state batteries. One topic deals with our proposed search framework based on a Bayesian optimization algorithm with a kernel definition that is general for high dimension of material descriptors, coupled to a DFT method to calculate ion migration energy barriers (Eb) over chemical search spaces of oxides (Eb as a search criterion) (Sci. Rep. 2018, 8, 5845). The next part shows our formulation of descriptors for crystalline solids which are derived from literature data and real feature values from atom-centered Voronoi partitioning (STAM 2018, 19, 231). We validated the scheme in terms of machine learning of various DFT-calculated properties such as cohesive energy, material density, electronic band gap energy, and convex hull decomposition energy.