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Density‐based clustering of crystal (mis)orientations and the orix Python library
Author(s) -
Johnstone Duncan N.,
Martineau Ben H.,
Crout Phillip,
Midgley Paul A.,
Eggeman Alexander S.
Publication year - 2020
Publication title -
journal of applied crystallography
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.429
H-Index - 162
ISSN - 1600-5767
DOI - 10.1107/s1600576720011103
Subject(s) - cluster analysis , python (programming language) , dbscan , orientation (vector space) , crystal twinning , cluster (spacecraft) , materials science , grain boundary , computer science , crystallography , geometry , artificial intelligence , mathematics , fuzzy clustering , chemistry , microstructure , canopy clustering algorithm , programming language , operating system
Crystal orientation mapping experiments typically measure orientations that are similar within grains and misorientations that are similar along grain boundaries. Such (mis)orientation data cluster in (mis)orientation space, and clusters are more pronounced if preferred orientations or special orientation relationships are present. Here, cluster analysis of (mis)orientation data is described and demonstrated using distance metrics incorporating crystal symmetry and the density‐based clustering algorithm DBSCAN. Frequently measured (mis)orientations are identified as corresponding to similarly (mis)oriented grains or grain boundaries, which are visualized both spatially and in three‐dimensional (mis)orientation spaces. An example is presented identifying deformation twinning modes in titanium, highlighting a key application of the clustering approach in identifying crystallographic orientation relationships and similarly oriented grains resulting from specific transformation pathways. A new open‐source Python library, orix , that enabled this work is also reported.