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Software systems and computational methods
Reference:
Burakov S.V., Zaloga A.N., Pan’kin S.I., Semenkin E.S., Yakimov I.S.
Applying a self-configuring genetic algorithm for modeling the atomic crystalline structure
of a chemical compounds using X-ray diffraction data
// Software systems and computational methods.
2014. ¹ 4.
P. 500-512.
URL: https://en.nbpublish.com/library_read_article.php?id=65867
Burakov S.V., Zaloga A.N., Pan’kin S.I., Semenkin E.S., Yakimov I.S. Applying a self-configuring genetic algorithm for modeling the atomic crystalline structure
of a chemical compounds using X-ray diffraction data
Abstract:
the article is devoted to evaluation of possibility and effectiveness of the use of
self-configuring genetic algorithm of global optimization to automate the task of determining
the atomic crystal structure of new substances by its powder the x-ray diffraction. The suggested
version of the self-configuring genetic algorithm was studied on the problem of determining the
known crystal structure of a Ba2CrO4 chemical compound, which required finding the location
of 7 independent atoms in the elementary cell of the crystal. To analyze the effectiveness and determining the convergence rate of structural models to the true structure of the substance
in the process of evolutionary search the authors performed several dozen launches of selfconfiguring
genetic algorithm with different population sizes of structural models and types
of genetic operations. The essence of the self-configuration method is in the fact that choice
of optimal genetic operators of selection, crossbreeding and mutation from the suggested
set of possible variants is performed by the self-configuring genetic algorithm itself while
solving the problem. The probability for the operators of being selected to generate the next
generation of population of structural models adapts based on the success of evolution by
using these operators on the previous generation. This leads to the automatic selection of
the best operators providing convergence of structural models to the true crystal structure.
One of the main problems that prevent the use of stochastic evolution of genetic algorithms
for structure analysis is the need for a non-trivial empirical selection of genetic operators.
Applying the self-configuring genetic algorithm to automate the selection of optimal genetic
operators in the task of modeling atomic crystal structure of chemical compounds by the X-ray
diffraction data is suggested for the first time. In determining the crystal structure of Ba2CrO4
using self-configuring genetic algorithm the convergence rate to the true crystal structure
reached 80%. This creates the possibility of developing an automated evolutionary genetic
algorithm for structural analysis based on the X-ray diffraction data.
Keywords:
evolutionary algorithms, genetic algorithms, self-configuration of genetic algorithms, crystal structure, X-ray powder diffraction, full-profile analysis, determination of crystal structure, self-configuration, diffraction pattern, genetic operators
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References
1. Semenkina M.E., Semenkin E.S. Programma dlya resheniya zadach simvol'noy regressii samokonfiguriruemym algoritmom geneticheskogo programmirovaniya. – M.: Rospatent. 2012. ¹ gos. reg. 2012619347.
2. Gumennikova A.V., Emel'yanova M.N., Semenkin E.S., Sopov E.A. Ob evolyutsionnykh algoritmakh resheniya slozhnykh zadach optimizatsii // Vestnik Sibirskogo gosudarstvennogo aerokosmicheskogo universiteta im. akademika M.F. Reshetneva. 2003. ¹ 4. S. 14.
3. Semenkin E., Semenkina M. Stochastic Models and Optimization Algorithms for Decision Support in Spacecraft Control Systems Preliminary Design // Informatics in Control, Automation and Robotics.-Lecture Notes in Electrical Engineering, Springer-Verlag, Berlin Heidelberg. 2014. Vol. 283. P. 51-65.
4. Burakov S.V., Semenkin E.S. Solving variational and Cauchy problems with self-configuring genetic programming algorithms // International Journal of Innovative Computing and Applications. 2013. Volume 5, Issue 3. P. 152–162.
5. Semenkin E., Semenkina M. The Choice of Spacecrafts' Control Systems Effective Variants with Self-Configuring Genetic Algorithm. In: Ferrier, J.L. et al (Eds.): Informatics in Control, Automation and Robotics: Proceedings of the 9th International Conference ICINCO’2012.– Rome: Italy. 2012. Vol. 1. P. 84-93.
6. Semenkin E.S., Semenkina M.E. Self-configuring Genetic Algorithm with Modified Uniform Crossover Operator // Advances in Swarm Intelligence. Lecture Notes in Computer Science 7331. – Springer-Verlag, Berlin Heidelberg. 2012. P. 414-421.
7. Meredig B., Wolverton C. A hybrid computational–experimental approach for automated crystal structure solution // Nature Materials. 2013. No. 12, P. 123–127.
8. Zaloga A. N., Burakov S. V., Semenkin E. S., Yakimov I. S.. A Research of Convergence of Multi-Population Binary and Real Genetic Algorithms for Solution of Crystal Structure from X-Ray Powder Diffraction Data // Applied Mechanics and Materials. Proceedings of APMSIT Conference. Shanghai, China. 2014.
9. Harris K. D. M. Powder Diffraction Crystallography of Molecular Solids // Top. Curr. Chem. 2012. No. 315. P. 133–177.
10. Harris K.D.M. Fundamentals and applications of genetic algorithms for structure solution from powder X-ray diffraction data // Computational Materials Science. 2009. V. 45. Issue 1. P. 16-20.
11. Yakimov Y. I., Semenkin E. S., Yakimov I. S. Two-level genetic algorithm for a fullprofile fitting of X-ray powder patterns // Z. Kristallogr. Suppl. 2009. No. 30. P. 21-26.
12. Griffin T.A.N., Shankland K., van de Streek J., Cole J. GDASH: a grid-enabled program for structure solution from powder diffraction data // J. Appl. Cryst. 2009. No. 42. P. 356-359.
13. Favre-Nicolin V., Cerny R. FOX, “free objects for crystallography”: a modular approach to ab initio structure determination from powder diffraction // J. Appl. Cryst. 2002. No. 35. P. 734-743.
14. David W.I.F., Shankland K. Structure determination from powder diffraction data // Acta Cryst. 2008. A64. P. 52-64.
15. Cerny R., Favre-Nicolin V. Direct space methods of structure determination from powder diffraction: principles, guidelines, perspectives // Z. Kristallogr. Suppl. 2007. No. 222. 105-113.
16. Young R.A. The Rietveld Method / Oxford University Press. 1995. 298 p.
17. Yakimov Ya.I., Kirik S.D., Semenkin E.S., Solov'ev L.A., Yakimov I.S. Evolyutsionnyy metod modelirovaniya kristallicheskoy struktury veshchestva po dannym poroshkovoy difraktsii // Zhurnal Sibirskogo federal'nogo universiteta, Khimiya. 2013. ¹6. S. 180-191.
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