Powder pattern indexing using the weighted crosscorrelation and genetic algorithms

X-ray diffraction is a powerful technique for investigating the structure of crystals and crystalline powders. Unfortunately, for powders, the first step in the structure elucidation process, retrieving the unit cell parameters (indexing), is still very critical. In the present article, an improved approach to powder pattern indexing is presented. The proposed method matches peak positions from experimental X-ray powder patterns with peak positions from trial cells using a recently published method for pattern comparison (weighted crosscorrelation). Trial cells are optimized with Genetic Algorithms. Patterns are not pretreated to remove any existing zero point shift, as this is determined during optimization. Another improvement is the peak assignment procedure. This assignment is needed for determining the similarity between lines from trial cells and experiment. It no longer allows calculated peaks to be assigned twice to different experimental peaks, which is beneficial for the indexing process. The procedure proves to be robust with respect to false peaks and accidental or systematic absensences of reflections, and is successfully applied to powder patterns originating from orthorhombic, monoclinic, and triclinic compounds measured with synchrotron as well as with conventional laboratory X-ray diffractometers.