International
Tables for
Crystallography
Volume H
Powder diffraction
Edited by C. J. Gilmore, J. A. Kaduk and H. Schenk

International Tables for Crystallography (2018). Vol. H, ch. 3.8, p. 325

Section 3.8.1. Introduction

C. J. Gilmore,a G. Barra and W. Donga*

aDepartment of Chemistry, University of Glasgow, University Avenue, Glasgow, G12 8QQ, UK
Correspondence e-mail:  chris@chem.gla.ac.uk

3.8.1. Introduction

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In high-throughput crystallography, crystallization experiments using robotics coupled with automatic sample changers and two-dimensional (2D) detectors can generate and measure over 1000 powder-diffraction patterns on a series of related compounds, often polymorphs or salts, in a day (Storey et al., 2004[link]). It is also possible to simultaneously measure spectroscopic data, especially Raman (Alvarez et al., 2009[link]). The analysis of these patterns poses a difficult statistical problem: a need to classify the data by putting the samples into clusters based on diffraction-pattern similarity so that unusual samples can be readily identified. At the same time, suitable visualization tools to help in the data-classification process are required; the techniques of classification and visualization go hand-in-hand. Interestingly, the techniques developed for large data sets with poor-quality data also have great value when looking at smaller data sets, and the visualization tools developed for high-throughput studies are especially useful when looking at phase transitions, mixtures etc.

In this chapter the methods for comparing whole patterns will be described. The mathematics of cluster analysis will then be explained, followed by a discussion of the associated visualization tools. Examples using small data sets from pharmaceuticals, inorganics and phase transitions will be given; the techniques used can be readily scaled up for handling large, high-throughput data sets. The same methods also work for spectroscopic data and the use of such information with and without powder X-ray diffraction (PXRD) data will be discussed. Finally, the use of visualization tools in quality control is demonstrated.

References

Alvarez, A. J., Singh, A. & Myerson, A. S. (2009). Polymorph screening: comparing a semi-automated approach with a high throughput method. Cryst. Growth Des. 9, 4181–4188.Google Scholar
Storey, R., Docherty, R., Higginson, P., Dallman, C., Gilmore, C., Barr, G. & Dong, W. (2004). Automation of solid form screening procedures in the pharmaceutical industry–how to avoid the bottlenecks. Crystallogr. Rev. 10, 45–56.Google Scholar








































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