Computational crystallography

The most notable advance in computational crystallography was the availability of methods for rehning protein structures by least-squares optimization. This developed in a number of laboratories and was made feasible by the implementation of fast Fourier transform techniques [32]. The most widely used system was PROLSQ from the Flendrickson lab [33]. 

Jones, T. A. (1981). FROIX) A graphics fitting program for macromolecules. In Computational Crystallography, Sayre, D., ed., pp 303-317. Qarendon Press, Oxford. 

Computational crystallography has today a much broader scope and meaning than it had 20 years ago, when it was stUl mostly restricted to algorithms and software for X-ray data processing. The following is a brief summary of highlights. 

As a final remark it may be stated that the predictive simulation of crystal nucleation from a solvent, and of the consequent polymorph selectivity, is the grand challenge of computational crystallography in the next few decades. 

In the last few years rapid advances have been made in the field of computational crystallography, so that it is now possible to produce highly refined computer models of a wide variety of polymeric materials using X-ray diffraction data. Unfortunately, these achievements have been negated to some extent because the techniques used to collect the data for such refinement programs have not advanced at a comparable rate. In this contribution we describe a computer program which facilitates the reduction of intensity and d-spacing data obtained by the multiple film-pack method, and attempts to quantify the errors associated with such measurements. 

David Sayre, Computational Crystallography, Clarendon Press, Oxford, UK, 1982. 

Rollett, J. S. Basic processes involved in least-squares and Fourier refinement. In Computational Crystallography. (Ed., Sayre, D.). pp. 338-353. Clarendon Press Oxford (1982). 

S. Baroni (2005) First-principles codes for computational crystallography in the Quantum-ESPRESSO package. Zeitschrift fiir Kristallogra,phie 220, p. 574... 

The Computational Crystallography Toolbox crystallographic algorithms in a reusable software framework, R. W. Grosse Kunstleve, N. K. Sauter, N. W. Moriarty, P. D. Adams, J. Appl. Crystallogr., 2002, 35, 126 136 A real space computer based... 

Ernest Orlando Lawrence Berkeley National Laboratory, Computational Crystallography Initiative, http //cci.lbl.gov/. 

The H atom is easy to locate computationally but difficult experimentally even neutron diffraction (see Chapter 5) often cannot pinpoint the exact position of the hydrogens in H2 ligands. Computational crystallography is a low-cost, high-quality technique for structural location and prediction of classical versus nonclassical structures. 

Crystal structures can indeed be predicted, if the word is taken in a sensible meaning. Compare this situation with astrophysics we know mass, age, distribution, speed, and spectral properties of galaxies, although we do not (and we do not need to) know the absolute position of each star in a galaxy. In a similar fashion, computational crystallography can easily predict the essential parameters of the bulk texture of organic matter in the solid state, although it cannot tell the exact position of each atom in a crystal structure. 

Dialog at question time in a computational crystallography meeting. 

Grosse-Kunstleve R W, Sauter N K, Moriarty N W and Adams P D (2002) The computational crystallography toolbox crystallographic algorithms in a reusable software framework, J Appl Gryst 35 126-136. 

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