Hostname: page-component-78c5997874-xbtfd Total loading time: 0 Render date: 2024-11-15T11:14:45.015Z Has data issue: false hasContentIssue false
  • English
  • Français

Determination of the [Pt(NH3)5Cl]Br3 crystal structure from X-ray powder diffraction data using multi-population genetic algorithm

Published online by Cambridge University Press:  28 February 2017

A. N. Zaloga ,
I. S. Yakimov  and
P. S. Dubinin
A. N. Zaloga*
Affiliation:
Siberian Federal University, Krasnoyarsk, Russia
I. S. Yakimov
Affiliation:
Siberian Federal University, Krasnoyarsk, Russia
P. S. Dubinin
Affiliation:
Siberian Federal University, Krasnoyarsk, Russia
*
a)Author to whom correspondence should be addressed. Electronic mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

The paper describes an approach for automated crystal structure solution from powder diffraction data using the multi-population genetic algorithm (MPGA). The advantage of using co-evolution with the best individual exchange, compared with the using of the evolution with a single genetic algorithm without interpopulation exchange, is shown. As an example, the paper describes the use of MPGA for solving the [Pt(NH3)5Cl]Br3 crystal structure, having the tetragonal I41/a space group [a = 17.2587(5) Å, c = 15.1164(3) Å, Z = 16, unit-cell volume V = 4502.61(10) Å3]. The MPGA convergence charts and the atomic positions distribution maps of the MPGA populations are given. The description of the final structure solution is also shown.

Keywords

X-ray powder diffractioncrystal structure analysis in direct spacegenetic algorithmsfull-profile analysisRietveld method
Type
Technical Articles
Information
Powder Diffraction , Volume 32 , Supplement S1 , September 2017 , pp. S110 - S117
Check for updates
Copyright
Copyright © International Centre for Diffraction Data 2017 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Albesa-Jové, D., Kariuki, B. M., Kitchin, S. J., Grice, L., Cheung, E. Y. and Harris, K. D. M. (2004). “Challenges in direct-space structure determination from powder diffraction data: a molecular material with four independent molecules in the asymmetric unit,” ChemPhysChem 5, 414–418.CrossRefGoogle Scholar
Altomare, A., Caliandro, R., Camalli, M., Cuocci, C., Giacovazzo, C., Moliterni, A. G. G. and Rizzi, R. (2004). “Automatic structure determination from powder data with EXPO2004,” J. Appl. Crystallogr. 37, 1025–1028.Google Scholar
Andreev, Y. G. and Bruce, P. G. (1988). “Solving crystal structures of molecular solids without single crystals: a simulated annealing approach,” J. Chem. Soc. Dalton Trans. 24, 4071–4080.Google Scholar
Andreev, Y. G., Lightfoot, P. and Bruce, P. G. (1997). “A new Monte Carlo approach to structure solution from powder data,” J. Appl. Crystallogr. 30, 294–305.CrossRefGoogle Scholar
Bevan, D. J. M., Drennan, J. and Rossell, H. J. (1982). “Structure determination of the fluorite-related superstructure phases Er10W2O21 and Y10W2O21,” Acta Crystallogr. B38, 2991–2997.Google Scholar
Burakov, S. V., Zaloga, A. N., Semenkin, E. S. and Yakimov, I. S. (2015). “Research on convergence of multipopulation binary- and real-coded genetic algorithms for solution of crystal structure from X-ray powder diffraction data,” Cryst. Res. Technol. 50, 724–728.Google Scholar
Chernyaev, I. I. (1964). Synthesis of Complex Compounds of Platinum Group Metals. Directory (Science, Moscow), pp. 160–161.Google Scholar
David, W. I. F., Shankland, K. and Shankland, N. (1998). “Routine determination of molecular crystal structures from powder diffraction data,” Chem. Commun. 8, 931–932.Google Scholar
David, W. I. F., Shankland, K., Van de Streek, J., Pidcock, E., Motherwell, W. D. S. and Cole, J. C. (2006). “DASH: a program for crystal structure determination from powder diffraction data,” J. Appl. Crystallogr. 39, 910–915.CrossRefGoogle Scholar
Falahiazar, L., Teshnehlab, M. and Falahiazar, A. (2012). “Parallel Genetic Algorithm based on a new migration strategy,” Proc. of Int. Conf. on Recent Advances in Computing and Software Systems. 37–41.Google Scholar
Favre-Nicolin, V. and Černý, R. (2002). “FOX, ‘Free Objects for crystallography’: a modular approach to ab initio structure determination from powder diffraction,” J. Appl. Crystallogr. 35, 734–743.CrossRefGoogle Scholar
Feng, Z. J. and Dong, C. (2007). “GEST: a program for structure determination from powder diffraction data using a genetic algorithm,” J. Appl. Crystallogr. 40, 583–588.Google Scholar
Griffin, T. A. N., Shankland, K., van de Streek, J. and Cole, J. (2009a). “GDASH: a grid-enabled program for structure solution from powder diffraction data,” J. Appl. Crystallogr. 42, 356–359.Google Scholar
Griffin, T. A. N., Shankland, K., van de Streek, J. and Cole, J. (2009b). “MDASH: a multi-core-enabled program for structure solution from powder diffraction data,” J. Appl. Crystallogr. 42, 360–361.Google Scholar
Habershon, S., Harris, K. D. M. and Johnston, R. L. (2003). “Development of a multipopulation parallel genetic algorithm for structure solution from powder diffraction data,” J. Comput. Chem. 24, 1766–1774.CrossRefGoogle ScholarPubMed
Harris, K. D. M., Johnston, R. L., and Kariuki, B. M. (1998). “The genetic algorithm: foundations and applications in structure solution from powder diffraction data,” Acta Crystallogr. A, Found. Crystallogr. 54, 632–645.Google Scholar
Kariuki, B. M., Zin, D. M. S., Tremayne, M. and Harris, K. D. M. (1996). “Crystal structure solution from powder X-ray diffraction data: the development of Monte Carlo methods to solve the crystal structure of the gamma-phase of 3-chloro-trans-cinnamic acid,” Chem. Mater. 8, 565.Google Scholar
Kariuki, B. M., Serrano-González, H., Johnston, R. L. and Harris, K. D. M. (1997). “The application of genetic algorithm for solving crystal structures from powder diffraction data,” Chem. Phys. Lett. 280, 189–195.Google Scholar
Kurose, S., Yamamori, K., Aikawa, M. and Yoshihara, I. (2012). “Asynchronous migration for parallel genetic programming on a computer cluster with multi-core processors,” Artif. Life Robot. 16, 533–536.Google Scholar
Le Bail, A. and Cranswick, L. M. D. (2009). “Third structure determination by powder diffractometry round robin (SDPDRR-3),” Powder Diffr. 24, 254–262.CrossRefGoogle Scholar
Lutterotti, L. and Bortolotti, M. (2003). “Object oriented programming and fast computation techniques in Maud, a program for powder diffraction analysis written in java,” IUCr: Compcomm Newsl. 1, 43–50.Google Scholar
Marchand, R., Piffard, Y. and Tournoux, M. (1975). “Structure cristalline de l'orthostannate de potassium K4SnO4 ,” Acta Crystallogr. B31, 511–514.Google Scholar
Martens, K.-P. and Hoppe, R. (1978). “Neue Oxoplumbate(II) A2PbO2 (A = K, Rb, Cs) mit zweikernigen Gruppen [OPbO2PbO],” Z. Anorg. Allg. Chem. 440, 81–104.Google Scholar
Mattausch, H. and Müller-Buschbaum, H. (1974). “Zur Kristallstruktur von Ba2CrO4 ,” Z. Anorg. Allg. Chem. 407, 129–134.Google Scholar
Meredig, B. and Wolverton, C. (2013). “A hybrid computational–experimental approach for automated crystal structure solution,” Nat. Mater. 12, 123–127.Google Scholar
Nalepa, J. and Blocho, M. (2014). “Co-operation in the Parallel Memetic Algorithm,” Int. J. Parallel Program. 43, 1–28.Google Scholar
Ozkan, O., Ermis, M. and Bekmezci, I. (2015). “A hybrid matheuristic approach for designing reliable wireless multimedia sensor networks,” Proc. of the Companion Publication of the 2015 Annual Conf. on Genetic and Evolutionary Computation, pp. 875–882.Google Scholar
Pennington, W. T. (1999). “DIAMOND – visual crystal structure information system,” J. Appl. Crystallogr. 32, 1028–1029.Google Scholar
Rohlíček, J., Hušák, M. and Favre-Nicolin, V. (2010). “Fox.Grid distributed computing,” http://fox.vincefn.net/Manual/Fox.Grid Google Scholar
Shankland, K., David, W. I. F. and Csoka, T. (1997). “Crystal structure determination from powder diffraction data by the application of a genetic algorithm,” Z. Kristallogr. 212, 550–552.CrossRefGoogle Scholar
Shankland, K., Spillman, M. J. and Kabova, E. A. (2013). “The principles underlying the use of powder diffraction data in solving pharmaceutical crystal structures,” Acta Crystallogr. 69, 1251–1259.Google Scholar
Solovyov, L. A. (2004). “Full-profile refinement by derivative difference minimization,” J. Appl. Crystallogr. 37, 743–749.Google Scholar
Spek, A. L. (2003). “Single-crystal structure validation with the program PLATON,” J. Appl. Crystallogr. 36, 7–13.Google Scholar
To, C. and Elati, M. (2013). “A parallel Genetic Programming for Single Class Classification,” Proc. of the 15th Annual Conf. Companion on Genetic and Evolutionary Computation, pp. 1579–1586.Google Scholar
Whitfield, P. S., Davidson, I. J., Mitchell, L. D., Wilson, S. A. and Mills, S. J. (2010). “Problem solving with the TOPAS macro language: corrections and constraints in simulated annealing and rietveld refinement,” Mater. Sci. Forum 651, 11–25.Google Scholar
Xia, Z., Molokeev, M. S., Oreshonkov, A. S., Atuchin, V. V., Liu, R.-S. and Dong, C. (2014). “Crystal and local structure refinement in Ca2Al3O6F explored by X-ray diffraction and Raman spectroscopy,” Phys. Chem. Chem. Phys. 16, 5952–5957.Google Scholar
Yakimov, Y. I., Kirik, S. D., Semenkin, E. S., Solovyov, L. A. and Yakimov, I. S. (2013). “The evolutionary method of modeling the crystal structure of substances from powder diffraction data,” J. Siber. Fed. Univ., Chem. 6, 180–191.Google Scholar
Zaloga, A. N., Burakov, S. V., Semenkin, E. S. and Yakimov, I. S. (2014). “Multi-population genetic algorithm for determination the crystal structure of substances from X-ray diffraction data,” J. Siber. Fed. Univ., Chem. 7, 572–580.Google Scholar
Supplementary material: File

Zaloga supplementary material

Zaloga supplementary material 1

Download Zaloga supplementary material(File)
File 47.6 KB
Supplementary material: File

Zaloga supplementary material

Zaloga supplementary material 2

Download Zaloga supplementary material(File)
File 48 KB