Accelerated Materials Design of Lithium Superionic Conductors Based on First-Principles Calculations and Machine Learning Algorithms

A method for efficiently screening a wide compositional and structural phase space of LISICON‐type superionic conductors is presented that utilizes a machine‐learning technique for combining theoretical and experimental datasets. By iteratively performing systematic sets of first‐principles calculations and focused experiments, it is shown how the materials design process can be greatly accelerated, suggesting potentially superior candidate lithium superionic conductors.