  Crystal structure prototype data have become a useful source of information
for materials discovery in the fields of crystallography, chemistry, physics,
and materials science. This work reports the development of a robust and
efficient method for assessing the similarity of structures on the basis of
their interatomic distances. Using this method, we proposed a simple and
unambiguous definition of crystal structure prototype based on hierarchical
clustering theory, and constructed the Crystal Structure Prototype Database
(CSPD) by filtering the known crystallographic structures in a database. With
similar method, a program Structure Prototype Analysis Package (SPAP) was
developed to remove similar structures in CALYPSO prediction results and
extract predicted low energy structures for a separate theoretical structure
database. A series of statistics describing the distribution of crystal
structure prototypes in the CSPD was compiled to provide an important insight
for structure prediction and high-throughput calculations. Illustrative
examples of the application of the proposed database are given, including the
generation of initial structures for structure prediction and determination of
the prototype structure in databases. These examples demonstrate the CSPD to be
a generally applicable and useful tool for materials discovery.
