Identification of Crystal Symmetry from Noisy Diffraction Patterns by A Shape Analysis and Deep Learning

The robust and automated determination of crystal symmetry is of utmost importance in material characterization and analysis. Recent studies have shown that deep learning (DL) methods can effectively reveal the correlations between X-ray or electron-beam diffraction patterns and crystal symmetry... (read more)

PDF Abstract

Tasks


Datasets


  Add Datasets introduced or used in this paper

Results from the Paper


  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.

Methods used in the Paper


METHOD TYPE
Concatenated Skip Connection
Skip Connections
Softmax
Output Functions
ReLU
Activation Functions
Batch Normalization
Normalization
Kaiming Initialization
Initialization
Convolution
Convolutions
Average Pooling
Pooling Operations
Dropout
Regularization
1x1 Convolution
Convolutions
Dense Connections
Feedforward Networks
Max Pooling
Pooling Operations
Dense Block
Image Model Blocks
Global Average Pooling
Pooling Operations
DenseNet
Convolutional Neural Networks