They achieved high classification performance despite data deterioration due to Poisson noise and instrumental resolution. classified crystal systems and space groups by applying a convolutional neural network (CNN) to simulated powder XRD patterns. Among various subtopics such as pattern decomposition and phase identification 15, 16, 17, 18 cluster analysis and phase mapping 19, 20, 21, 22, 23, similarity metrics for comparison of diffraction data 24, 25, 26 classification of a crystal symmetry 27, 28, 29, 30, 31, 32, 33, a paper by Park et al. Therefore, we focus on the classification of crystal systems and space groups using machine learning (ML) approaches, inspired by the fact that experienced researchers can guess the crystal system from a given diffraction pattern.Īpplication of ML and related techniques for diffraction data analysis is a hot research topic in recent times 13, 14. Excluding human involvement in these processes as far as possible improves the situation and helps realise high-throughput (HiTp) experiments. Given that a large number of powder XRD patterns are generated daily at synchrotron facilities around the world, these time-consuming processes performed manually by human experts are obvious bottlenecks in materials research 11, 12, 13.
POWDER X RAY DIFFRACTION MANUAL
While the most arduous step is structure refinement using the Rietveld method 10, which typically requires manual optimisation of tens of parameters, space group determination at the initial stage of structure analysis also needs manual trial-and-error operations frequently.
Decoding powder diffraction patterns to crystal structure information involves several steps, such as peak indexing, space group determination, initial parameter estimation for the crystal structure, and structure refinement 4, 5, 6, 7, 8, 9. Powder X-ray diffraction (XRD) and powder neutron diffraction are principal experimental techniques to elucidate crystal structures data obtained using these techniques are stored in various databases for specific classes of materials, for instance, inorganic materials and proteins 3. A crystal structure is defined in terms of lattice symmetry, lattice parameters, the types and positions of atoms, and site occupancy. Crystal structure characterisation is one of the most important tasks in materials development because crystal structure determines material properties 1, 2.
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