The cubic crystal system was chosen as a model system and five space groups of 213, 221, 225, 227, and 229 in the cubic system were selected for the test and validation, based on the distinguishability of the SAD patterns. Acceleration voltages, zone axes, and camera lengths were used as variables and crystal information format (CIF) files obtained from open crystal data repositories were used as inputs. The dataset required for training and validating the ResNet architectures was obtained by the computer simulation of the selected area electron diffraction (SAD) in transmission electron microscopy. Investigations have been made to explore the applicability of an off-the-shelf deep convolutional neural network (DCNN) architecture, residual neural network (ResNet), to the classification of the crystal structure of materials using electron diffraction patterns without prior knowledge of the material systems under consideration.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |