Premium
GIDInd : an automated indexing software for grazing‐incidence X‐ray diffraction data
Author(s) -
Kainz Manuel Peter,
Legenstein Lukas,
Holzer Valentin,
Hofer Sebastian,
Kaltenegger Martin,
Resel Roland,
Simbrunner Josef
Publication year - 2021
Publication title -
journal of applied crystallography
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.429
H-Index - 162
ISSN - 1600-5767
DOI - 10.1107/s1600576721006609
Subject(s) - diffraction , triclinic crystal system , search engine indexing , graphical user interface , computer science , software , crystallography , lattice constant , electron backscatter diffraction , algorithm , materials science , optics , crystal structure , chemistry , physics , artificial intelligence , programming language
Grazing‐incidence X‐ray diffraction (GIXD) is a widely used technique for the crystallographic characterization of thin films. The identification of a specific phase or the discovery of an unknown polymorph always requires indexing of the associated diffraction pattern. However, despite the importance of this procedure, only a few approaches have been developed so far. Recently, an advanced mathematical framework for indexing of these specific diffraction patterns has been developed. Here, the successful implementation of this framework in the form of an automated indexing software, named GIDInd , is introduced. GIDInd is based on the assumption of a triclinic unit cell with six lattice constants and a distinct contact plane parallel to the substrate surface. Two approaches are chosen: (i) using only diffraction peaks of the GIXD pattern and (ii) combining the GIXD pattern with a specular diffraction peak. In the first approach the six unknown lattice parameters have to be determined by a single fitting procedure, while in the second approach two successive fitting procedures are used with three unknown parameters each. The output unit cells are reduced cells according to approved crystallographic conventions. Unit‐cell solutions are additionally numerically optimized. The computational toolkit is compiled in the form of a MATLAB executable and presented within a user‐friendly graphical user interface. The program is demonstrated by application on two independent examples of thin organic films.