Accelerating discovery and design of high-performance solid-state electrolytes: a machine learning approach

is identified to have the second-lowest migration barrier height of 0.365 eV. By focusing the training of the machine learning model on a specific class of materials, our approach significantly reduces the time, resources, and size of the dataset required to discover novel materials with targeted properties. This methodology is readily adaptable to the design of materials in various other fields, including catalysis and structural materials.