A new Skoltech patent: better, faster crystal structure prediction for new materials

Skoltech’s Artem R. Oganov and Pavel Bushlanov as well as their coauthor Vladislav Blatov from the Samara State Technical University have been granted a patent for a topology-based crystal structure generator, a promising method of crystal structure prediction that can significantly enhance computational materials discovery.

 patent-2726899Two main approaches to computational materials discovery – those based on data mining and those based on global optimization – are largely complementary. The weakness of many data mining approaches is that they are confined to databases of experimentally or theoretically produced structures, which are usually very incomplete, and cannot predict new crystal structures. However, this difficulty can be overcome by using a database of topological types, such that from one topological type one can derive an infinite number of crystal structures.

Using this topological method, the researchers designed and tested a new hybrid approach to crystal structure prediction, combining the best features from global optimization and data mining methods. They showed that topology-based crystal structure generation is more efficient than conventional global optimization, with threefold increase of efficiency in the performed tests. 

“Our topological structure generator is not limited by the existing databases of structure types and is capable of generating an infinite number of new crystal structure types from a finite set of underlying topologies and group–subgroup relations,” the authors write in a 2019 paper for the journal Computer Physics Communications that describes their invention.

The new generator is already integrated into USPEX, a world-renowned evolutionary algorithm developed by Oganov and his students, but it can also be used within other approaches.


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