Parallel genetic algorithms for crystal structure prediction: Successes and failures in predicting bicalutamide polymorphs
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 findin...
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| Otros Autores: | , |
| Formato: | Acta de conferencia Capítulo de libro |
| Lenguaje: | Inglés |
| Publicado: |
2009
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| Acceso en línea: | Registro en Scopus DOI Handle Registro en la Biblioteca Digital |
| Aporte de: | Registro referencial: Solicitar el recurso aquí |
| Sumario: | 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. © 2009 Springer Berlin Heidelberg. |
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| ISBN: | 3642040691 9783642040696 |
| ISSN: | 03029743 |
| DOI: | 10.1007/978-3-642-04070-2_14 |