Identifying superionic conductors by materials informatics and high-throughput synthesis

Combinatorial chemistry has been proven effective in the search for novel functional materials, especially in the field of organic chemistry, and is being used to identify functional inorganic compounds. However, there is a growing need for approaches that predict and experimentally realize new materials, beyond composition optimization of known systems. Application of combinatorial chemistry to materials discovery is typically hindered by a limited ability to search a wide chemical composition space, and by our ability to experimentally screen promising compounds. Here, a combinatorial scheme is proposed that combines a materials informatics technique to define a chemical search space with high-throughput synthesis and evaluation. We identify high-performance superionic conductors in the Ca-(Nb,Ta)-Bi-O system, demonstrating the effectiveness of this approach for accelerated materials discovery. High-throughput prediction and synthesis are vital for obtaining new materials that deviate from existing compositions. Here, machine learning is combined with high-throughput synthesis to identify superionic conductors based on Ca-(Nb,Ta)-Bi-O.