Prediction of Lattice Volumes of Crystal Samples by Computer Image Recognition on the X-ray Diffraction Patterns

Abstract

Computer image recognition (CIR) on the diffraction patterns of X-ray single crystal diffractometer was used to assist the analysis of the unit cell parameters in terms of lattice volume. This method can improve the prediction accuracy of unit cell parameters in the pre-experiment step since erroneous prediction by the software may occur for 5% tested crystal samples, which may critically affect the complete crystal data quality and cause unnecessary testing time. In the imaging processing, the diffraction pattern images saved as .img format files were firstly batch transferred into .jpg format with high quality for further treatment. Specific features of the crystal diffraction points or zones in the images were extracted for the recognition processing by color mode. The Findcontour algorithm and the Canny algorithm based on Gaussian filtering were used for the diffraction peak points and area detection. The relationship between the distribution of crystal diffraction points (DCDP) and the lattice volumes based on reciprocal lattice theory was established for the users to judge the reasonable location range of the lattice volumes. A Density model was proposed to correlate the dependence of the DCDP and the experimental data. Special crystal samples were chosen to test the CIR performance, and its accuracy for the lattice volume prediction was evaluated. The CIR method can be used for the assistance in the analysis of the lattice volume location range and for crystal structure solving.