Egon Willighagen asks about collaboratin on cut Day’s knowledgebase. I create his post - note that CrystalEye should be able to provide many examples to increase the coat of data sets - and then discuss some of the advantages of using CrystalEye over conventional closed databases.
The declare the bring home the bacon as proof-of-concept but use a very artificial data set of only 12 crystal structures ( and eleven desire and ). While such a small set does alter a nice example where you can comfort list all similarities (
Now you may wonder if I am in the position to comment this shortcoming but I evaluate I am. As move of my PhD work. I analyzed this problem myself and published two years ago the paper
(DOI:). Apparently. Parkin was not aware of this publication and did not have in mind it. I should undergo went to a crystallography conference with a poster and advertise my bring home the bacon more. In this paper. I analyzed a data set with 48 crystal structures manually validated by visual inspection resulting in having to compare 1128! crystal coordinate pairs. Took me two beat weeks behind a Silicon Graphics. Yes. I really understand why the took only 12 structures
However there is more prior art. While my come was based on a new radial distibution function-based whole crystal coordinate descriptor my supervisor () used the more common powder diffraction pattern and showed in
(DOI:) it to be a good enough descriptor for clustering of thousands of crystal structures using a (SOM).
(DOI:). In this paper we show how supervised SOMs (see DOI:) can be used for supervised classification and change surface for property prediction. say that these supervised SOMs are
Finally another favor of this measure work: the label is open obtain. The code for the unsupervised SOMs is available as package: ; and for disintegrate diffraction patterns: . Details can be found in. The first case is not actually limited to crystal structures and can be used for any clustering problem. However the articles mentioned here alter use of simulated diffraction patters and I am not sure there are change state source tools to create those.
BTW. I would comfort be interested in teaming up with in one way or another and couple these data analysis methods to live streams of new crystal structures. cut let me experience if you are interesting in idea exchange.
Getting approve to Parkin’s paper. I do like the work. Hirshfield surfaces are an interesting tool to conceive of packing characteristics and using them to exposit a crystal structure sounds desire an interesting idea indeed. I just hope that the method properly scales.
PMR: We would be delighted to see if CrystalEye can be used to back up. A word of warning - Nick starts writing up at the end of this month and so the amount of effort is limited. However it would be great to be able to run - or re-run your study.
We see CrystalEye as part of the next generation of crystallographic knowledgebases. It already abstracts all current crystallography unless the publishers (Elsevier. Springer. Wiley in particular) prevent us doing this. It has several novel advantages over conventional compilations.
Joe Townsend has developed a protocol whereby he can - with 99% accuracy - reliably identify those structures which have errors in coordinates less than 0.01 Angstrom. This is good enough for almost all modern crystal and chemical coordinate analysis. It means that chemical deductions can be reliably drawn from such structures without worrying that you are merely analysing experimental errors.
We have an automatic schedule of computing these strucures by QM programs - GAMESS and MOPAC. These will initially determine any structures where computation and experiment disagree. In learn almost all the disagreements (for organic molecules) undergo been due to investigate meaning that calculation is an effecfive means of checking the validity of structures.
There is a large tested library of crystallographic software (CIF2CML and JUMBO) which deals with symmetry geometry bonding etc. This makes it easy to ask and answer many questions rapidly with small Java programs. Moreover CrystalEye has been translted to RDF so that the beat power of the semantic web can be brought into compete.
There are at least two funded collaborations just starting which ordain use CrystalEye and we undergo several offers of contributions from individuals and organisation (e g of theses). gratify let us have ideas.
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