  Recognition of structure prototypes from tremendous known inorganic crystal
structures has been an important subject beneficial for material science
research and new materials design. The existing databases of inorganic crystal
structure prototypes were mostly constructed by classifying materials in terms
of the crystallographic space group information. Herein, we employed a distinct
strategy to construct the inorganic crystal structure prototype database,
relying on the classification of materials in terms of local atomic
environments (LAE) accompanied by unsupervised machine learning method.
Specifically, we adopted a hierarchical clustering approach onto all
experimentally known inorganic crystal structures data to identify structure
prototypes. The criterion for hierarchical clustering is the LAE represented by
the state-of-the-art structure fingerprints of the improved bond-orientational
order parameters and the smooth overlap of atomic positions. This allows us to
build up a LAE-based Inorganic Crystal Structure Prototype Database (LAE-ICSPD)
containing 15,613 structure prototypes with defined stoichiometries. In
addition, we have developed a Structure Prototype Generator Infrastructure
(SPGI) package, which is a useful toolkit for structure prototype generation.
Our developed SPGI toolkit and LAE-ICSPD are beneficial for investigating
inorganic materials in a global way as well as accelerating materials discovery
process in the data-driven mode.
