Pattern‐matching indexing of Laue and monochromatic serial crystallography data for applications in materials science. Issue 3 (29th May 2020)

• Record Type: Journal Article
• Authors: Dejoie, Catherine; Tamura, Nobumichi
• Is Part Of: Journal of applied crystallography. Volume 53:Issue 3(2020)
• Page Start: 824
• Page End: 836
• Publication Date: 2020-05-29
• Subjects: indexing -- energy bandpass -- Laue microdiffraction -- pattern matching -- serial crystallography; Crystallography -- Periodicals; 548.05
• DOI: 10.1107/S160057672000521X
• Languages: English
• ISSNs: 0021-8898

Abstract

Abstract : An algorithm based on the matching of q vector pairs is combined with three‐dimensional pattern matching using a nearest‐neighbors approach to index Laue and monochromatic serial crystallography data recorded on small‐unit‐cell samples. Abstract : Serial crystallography data can be challenging to index, as each frame is processed individually, rather than being processed as a whole like in conventional X‐ray single‐crystal crystallography. An algorithm has been developed to index still diffraction patterns arising from small‐unit‐cell samples. The algorithm is based on the matching of reciprocal‐lattice vector pairs, as developed for Laue microdiffraction data indexing, combined with three‐dimensional pattern matching using a nearest‐neighbors approach. As a result, large‐bandpass data ( e.g. 5–24 keV energy range) and monochromatic data can be processed, the main requirement being prior knowledge of the unit cell. Angles calculated in the vicinity of a few theoretical and experimental reciprocal‐lattice vectors are compared, and only vectors with the highest number of common angles are selected as candidates to obtain the orientation matrix. Global matching on the entire pattern is then checked. Four indexing options are available, two for the ranking of the theoretical reciprocal‐lattice vectors and two for reducing the number of possible candidates. The algorithm has been used to index several data sets collected under different experimental conditions on a series of model samples. Knowing the crystallographic structure of the sample and using this information to rank the theoretical reflections based on the structure factors helps the indexing of large‐bandpass data for the largest‐unit‐cell samples. For small‐bandpass data, shortening the candidate list to determine the orientation matrix should be based on matching pairs of reciprocal‐lattice vectors instead of triplet matching.