English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Recognition of structure prototypes from tremendous known inorganic crystalstructures has been an important subject beneficial for material scienceresearch and new materials design. The existing databases of inorganic crystalstructure prototypes were mostly constructed by classifying materials in termsof the crystallographic space group information. Herein, we employed a distinctstrategy to construct the inorganic crystal structure prototype database,relying on the classification of materials in terms of local atomicenvironments (LAE) accompanied by unsupervised machine learning method.Specifically, we adopted a hierarchical clustering approach onto allexperimentally known inorganic crystal structures data to identify structureprototypes. The criterion for hierarchical clustering is the LAE represented bythe state-of-the-art structure fingerprints of the improved bond-orientationalorder parameters and the smooth overlap of atomic positions. This allows us tobuild up a LAE-based Inorganic Crystal Structure Prototype Database (LAE-ICSPD)containing 15,613 structure prototypes with defined stoichiometries. Inaddition, 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 investigatinginorganic materials in a global way as well as accelerating materials discoveryprocess in the data-driven mode.

Inorganic Crystal Structure Prototype Database based on Unsupervised Learning of Local Atomic Environments