Construction of crystal structure prototype database: methods and applications

Abstract

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.

arXiv comment20 pages, 8 figures

References in corpus (5)

• CALYPSO: a method for crystal structure prediction
• Crystal Structure Prediction via Particle Swarm Optimization
• Predicting Crystal Structures with Data Mining of Quantum Calculations
• Crystal structure prediction using the Minima Hopping method
• A fingerprint based metric for measuring similarities of crystalline structures

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• TCSP: a Template based crystal structure prediction algorithm and web server for materials discovery