Crystal structure prototype database based on machine learning classification of existing inorganic material structures

Combining high-throughput calculations with database construction and data mining offers opportunities for computational material scientists to discover new materials. Candidate materials considered in high-throughput calculations are usually from chemical substitution or structure variation based on known crystal structures. So, knowledge of crystal structure prototypes is vital for the validity of high-throughput calculations. We herein built a high quality crystal structure prototype database with the aid of machine learning classification of existing inorganic materials structures. The structure data were classified by the hierarchical clustering approach and the state-of-the-art structure fingerprinters including the bond order parameters and the smooth overlap of atomic positions were used for structure characterization. The database can generate sub-databases dynamically based on new criteria. We have integrated the database into the in-house developed infrastructure of JUMP², a python framework for materials discovery via high-throughput calculations, aiming at creating an automatic and high-performance computational materials discovery platform.