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.Despite their promise,most of these studies have been limited to identifying relatively few classes into which a target material may be grouped.On the other hand,the DL-based identification of crystal symmetry suffers from a drastic drop in accuracy for problems involving classification into tens or hundreds of symmetry classes(e.g.,up to 230 space groups),severely limiting its practical usage.