Structure determination from powder data without prior indexing, using a similarity measure based on cross-correlation functions

Abstract

A method to refine organic crystal structures from powder diffraction data with incorrect lattice parameters has been developed. The method is based on the similarity measure developed by de Gelder et al. [J. Comput. Chem. (2001), 22, 273-289], using the cross- and auto-correlation functions of a simulated and an experimental powder pattern. The lattice parameters, molecular position, molecular orientation and selected intramolecular degrees of freedom are optimized until the similarity measure reaches a maximum; subsequently, a Rietveld refinement is carried out. The program FIDEL (FIt with DEviating Lattice parameters) implements this method. The procedure is also suitable for unindexed powder data, powder diagrams of very low quality and powder diagrams of non-phase-pure samples. Various applications are shown, including structure determinations from powder data using crystal structure predictions by standard force-field methods. Other useful applications include the automatic structure determination from powder data starting from the crystal structures of isostructural compounds (e.g. a solvate, hydrate or chemical derivative), or from crystal data measured at a different temperature or pressure.

Same or different – that is the question: identification of crystal forms from crystal structure data

“…32 Schmidt et al has also used cross-correlation functions to compare powder patterns which has been implemented in the FIDEL software. 38 Both methods, integrated full-pattern analysis and cross-correlation functions, are very sensitive to impurities and preferred orientation and therefore are useful as pre-screening steps in automated, highthroughput lines for quality control and polymorph screening when one type of pattern is expected. On the contrary, their application may be less straight forward when comparing diffraction data collected under different conditions (although this appears to work well when comparing patterns calculated from CIF files).…”

Can computed crystal energy landscapes help understand pharmaceutical solids?

“…However, the powder pattern is very sensitive to the cell parameters and the anisotropic thermal expansion of organic crystals, and CSP generated structures will be in error typically by a few %, comparable to the neglected thermal expansion. Methods are being developed to facilitate the automatic use of CSP structures to help solve structures from powder data 55. There are many reported CSP-aided structure solutions from powders, ranging from cases where the powder pattern could not be indexed,56 through suggesting a set of possible structures that are consistent with pattern indexing, as discussed later for metastable forms of olanzapine16 and DB7,15 to correcting the proton positions 57.…”

Is zeroth order crystal structure prediction (CSP_0) coming to maturity? What should we aim for in an ideal crystal structure prediction code?

“…The combination of CSP and calculating (nonthermodynamic) properties, whether for targeting new functional materials or for structural characterisation, such as various diffraction 78 or solid state NMR experiments, 79 is really contributing to science. The question though is how much this relies on human scientic understanding interpreting the results of the CSP_0 (or CSP_thd) for experimental collaborators, or whether we can have a black-box computer code.…”