The application relates to the field of
crystal structure symmetry classification and retrieval, and specifically discloses a
crystal structure symmetry determination and retrieval method using
electron diffraction, which comprises the following steps: a multi-view selected area
electron diffraction data input module is used to acquire a two-zone axial
diffraction spectrum and perform pretreatment; a symmetry
feature coding module is used to extract a single-view descriptor through a double-
branch convolutional neural network, to obtain a material descriptor through view
pooling fusion; a hierarchical symmetry classification module is used to realize hierarchical classification of
crystal systems and space groups, and to guarantee the logic of the results through consistency constraints; a high-dimensional feature embedding and
database construction module is used to standardize
crystal structure files, to generate descriptors, and to construct a vector
database; and a constrained structure retrieval module is used to combine the classification results and user constraints to filter candidate structures, and to complete retrieval through
cosine similarity. The application solves the problems of poor generalization, insufficient multi-view
feature fusion and inability to associate a crystal
library in the prior art, and realizes full-process
automation of
crystal structure symmetry classification and retrieval.