MGAC (MODIFIED GENETIC ALGORITHM FOR CRYSTALS AND CLUSTERS): PREDICTION OF FLEXIBLE MOLECULES.

V. BAZTERRA, M. B. FERRARO, Y J. C. FACELLI

SAN LUIS

Congreso; IIII REUNION de la Asociación Argentina de Cristalografía; 2007

ASOCIACION ARGENTINA DE CRISTALOGRAFIA

In this presentation we describe a new distributed computing framework for crystal structure prediction that is capable of performing crystal structures searches for flexible molecules within any space group and within an arbitrary number of molecules in the asymmetric unit. The crystal structure prediction (CSP) of organic compounds can be described as the process of creating a list of crystal structure candidates, which are likely to be found experimentally, using only the molecular composition and connectivity as input information. Genetic Algorithms (GAs) are a family of search techniques rooted on the ideas of Darwinian evolution.[1] Operators analogue to crossover, mutation and natural selection are employed to perform a search able to explore and learn the multidimensional parameter space and determine which regions of the space provide good solutions to a problem. To improve their convergence GAs are commonly coupled with local optimizations at each generation. This combined approach has been implemented in our program MGAC (Modified Genetic Algorithm for Crynstal and Clusters).[2]  Crystal structure coding             When using GAs for the prediction of crystal structures, these structures have to be encoded in a “genome” that can be manipulated by the genetic operators as well as used to calculate the energy of the crystal. For the n-molecules in the asymmetric cell the MGAC uses their molecule center of mass , its orientation  and the values of the relevant dihedral angles   as the parameters, describing the molecular coordinates, included in the global optimisations. In addition it is necessary to specify the space group and lattice parameters to define a crystal. After each GA evolution all the new structures are relaxed by a local optimisation within the desired space group, creating a new set of candidate solutions that compete with the existing solutions in the population. For this presentation the MGAC calculations were done using 30 individuals, evolving for 100 generations with a mutation probability of 0.1 and a replacement factor of 0.5. Calculations were performed three times for each of the most common fourteen space groups: P1, P-1, P21, C2, Pc, Cc, P21/c, C2/c, P212121, Pca21, Pna21, Pbcn, Pbca and Pnma with one and two molecules per asymmetric unit cell, respectively. Energy evaluation and local optimisation             MGAC uses the CHARMM program [3] for the energy evaluation and local optimisation of the crystal structures.