PolySNAP 3 and PolySNAP M

PolySNAP 3 and PolySNAP M are software packages designed to match and analyse patterns utilising their full profiles. This allows for quick and accurate identification of samples, even when working with data of low, quality or where preferred orientation effects are significant.

The software provides an easy to use interface to several powerful and novel statistical methods to rank patterns in order of their similarity to any selected sample, allowing unknowns to be quickly identified. In quantitative mode, given a mixture pattern and potential pure phase patterns, it can identify which patterns are in the mixture, and quantify their proportions quickly and easily using a non-Rietveld based approach.

The matching procedure can be automated for high-throughput analysis, allowing for large datasets to be analysed in a single run. Highly flexible graphical output is provided to summarise and visualise the results. This highlights any unusual data, and means that time is not wasted looking at the very many patterns that behave exactly as expected. 

PolySNAP M is designed to work with Powder X-Ray Diffraction patterns, and small datasets. 

Its big brother, PolySNAP 3, can work on any one-dimensional data type - powder, Raman, DSC, IR, etc., as well as being able to utilise numeric data as well (e.g. melting points). These different techniques can be analysed individually, or much more powerfully, the results can be combined using an automatic weighting scheme to give an overall view of the dataset, which is invaluable to highlight any inconsistencies between different methods, as well as more accurate descriptions of what the samples are really like.

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dSNAP

dSNAP is a free software package to help users automatically classify and visualise the results of database searches using the Cambridge Structural Database.

With the explosion in high quality structural determinations in the area of small molecule crystallography, the problem of efficient mining of the CSD is very relevant for structural chemists. The CSD represents an enormously powerful resource, but faced with more than 300,000 structures, the attempt to extract meaningful chemical information can be daunting. Many search algorithms and data mining strategies already exist, but here we present a method of extracting chemical information from such datasets using cluster analysis.

By exporting the complete geometric information for a fragment of interest - all of the  distances and angles - dSNAP provides facilities for  automatically classifying and visualising the results of a search. It allows the user to assess those classifications chemically, diminishing the need for tedious manual classification. The real beauty of the method is in its interactivity and scalability - to go from getting an overview of the entire dataset and easily spot any outliers or errors, to the ability to ‘drill down’ within a cluster, looking at more and more detailed differences between different fragments.

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