Chem. Cryst., Oxford

CrystalsThe Chemical Crystallography Group and X-ray Crystallography Facility are located in the Chemistry Research Laboratory, Mansfield Road, Oxford. X-ray crystallography was established in Oxford in 1929 and has been part of the Department of Chemistry since 1946.

In addition to world-class X-ray diffraction facilities, Chem. Cryst. has an active research group and is home to the CRYSTALS software project.  We also have a number of projects available for undergraduate Part II chemists in the coming year.

Jul 182018
 
Crystals v6999

The CRYSTALS v6999 installer for Windows 7/10 is now available.

Please report problems. The version number (6999) refers to a specific snapshot of the source code enabling us to better identify and fix bugs.

Key changes between 14.6720 and 14.6999
Added some trial shortcut keys to the menus. Ctrl-O: open file dialog, Ctrl-R: setup … Read the rest

Jun 242018
 
An enhanced set of displacement parameter restraints in CRYSTALS (P. Parois, J. Arnold and R.I. Cooper)

Journal of Applied Crystallography, 2018, 51, 1059-1068. [ doi: 10.1107/S1600576718007100 ]

The implementation and use of a set of new displacement parameter restraints is described. Anisotropic displacement parameters account for a large proportion of the parameters in a crystallographic refinement, but very few restraints for conveniently controlling their values have been implemented in … Read the rest

Apr 052018
 
Prizes at the BCA Spring Meeting 2018 in Warwick

The 2018 Meeting of the British Crystallographic Association was held at Warwick University where Chem. Cryst. was well represented.  The meeting started with the Young Crystallographers Satellite meeting, during which Lewis Morgan’s oral presentation was so “eggsellent” that he won the Industrial Group Prize for the best talk, and with it, the dubious honour of … Read the rest

Nov 132017
 
Crystals v6720

The CRYSTALS v6720 installer is now available.

This update uses a new 64-bit Fortran compiler, fixes a number of bugs and eliminates a few mysterious crashes. Please report problems. The version number now refers to a specific snapshot of the source code enabling us to better identify and fix bugs.

Key changes between v14.6237 Read the rest

Nov 012017
 
Some Experimental Aspects of Absolute Configuration Determination using Single Crystal X-​Ray Diffraction (A. L. Thompson, S. F. Jenkinson & G. W. J. Fleet)

Tetrahedron Asymmetry 2017, 28(10), 1330-1336 [doi:10.1016/j.tetasy.2017.08.016]

Students of single crystal X-​ray diffraction are often give advice as to how best to collect their data when attempting absolute configuration determination.  These ‘rules’ often have more grounding in gut-​feeling than evidence.  Thus, in an effort to provide advice and evidence that today’s crystallographers can … Read the rest

Oct 232017
 
HUG and SQUEEZE: using CRYSTALS to incorporate resonant-scattering in the SQUEEZE structure factor contributions to determine absolute structure (R.I. Cooper, H.D. Flack and D.J. Watkin)Acta Crystallographica, 2017, C73, 845–853. [ doi:10.1107/S2053229617013304 ] Using an approximate correction to the X-ray scattering from disordered, resonantly scattering regions of crystal structures we have developed and tested a procedure (HUG) to recover the absolute structure using conventional Flack x refi nement or other post-re finement determination methods.
Sep 082017
 
IUCr 2017

The triennial congress of the International Union of Crystallography was held in Hyderabad over 8 days in August 2017.

During the meeting Richard Cooper presented recent work with Jerome Wicker:
Optimizing co-crystal  screens using a data-driven machine learning method

We also took an opportunity to present recent developments in the CRYSTALS software at the … Read the rest

Jul 062017
 
Will They Co-crystallize? (J.G.P. Wicker,  L.M. Crowley,  O. Robshaw,  E.J. Little,  S. Stokes,  R.I. Cooper  and  S.E. Lawrence)

CrystEngComm, 2017, 19, 5336 – 5340 [ doi:10.1039/C7CE00587C ]

A data-driven approach to predicting co-crystal formation reduces the number of experiments required to successfully produce new co-crystals. A machine learning algorithm trained on an in-house set of co-crystallization experiments results in a 2.6-fold enrichment of successful co-crystal formation in a ranked list of … Read the rest