Abstract
This paper describes the application of our distributed computing framework for crystal structure prediction, Modified Genetic Algorithms for Crystal and Cluster Prediction (MGAC), to predict the crystal structure of the two known polymorphs of bicalutamide. The paper describes our success in finding the lower energy polymorph and the difficulties encountered in finding the second one. The results show that genetic algorithms are very effective in finding low energy crystal conformations, but unfortunately many of them are not plausible due to spurious effects introduced by the energy potential function used in the selection process. We propose to solve this by using a multi objective optimization GA approach, adding the unit cell volume as a second optimization target.
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Ferraro, M.B., Orendt, A.M., Facelli, J.C. (2009). Parallel Genetic Algorithms for Crystal Structure Prediction: Successes and Failures in Predicting Bicalutamide Polymorphs. In: Huang, DS., Jo, KH., Lee, HH., Kang, HJ., Bevilacqua, V. (eds) Emerging Intelligent Computing Technology and Applications. ICIC 2009. Lecture Notes in Computer Science, vol 5754. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04070-2_14
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DOI: https://doi.org/10.1007/978-3-642-04070-2_14
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