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Evolutionary niching in the GAtor genetic algorithm for molecular crystal structure prediction

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Abstract

The goal of molecular crystal structure prediction (CSP) is to find all the plausible polymorphs for a given molecule. This requires performing global optimization over a high-dimensional search space. Genetic algorithms (GAs) perform global optimization by starting from an initial population of structures and generating new candidate structures by breeding the fittest structures in the population. Typically, the fitness function is based on relative lattice energies, such that structures with lower energies have a higher probability of being selected for mating. GAs may be adapted to perform multi-modal optimization by using evolutionary niching methods that support the formation of several stable subpopulations and suppress the over-sampling of densely populated regions. Evolutionary niching is implemented in the GAtor molecular crystal structure prediction code by using techniques from machine learning to dynamically cluster the population into niches of structural similarity. A cluster-based fitness function is constructed such that structures in less populated clusters have a higher probability of being selected for breeding. Here, the effects of evolutionary niching are investigated for the crystal structure prediction of 1,3-dibromo-2-chloro-5-fluorobenzene. Using the cluster-based fitness function increases the success rate of generating the experimental structure and additional low-energy structures with similar packing motifs.

Graphical abstract: Evolutionary niching in the GAtor genetic algorithm for molecular crystal structure prediction

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Publication details

The article was received on 19 Mar 2018, accepted on 03 Apr 2018 and first published on 03 Apr 2018


Article type: Paper
DOI: 10.1039/C8FD00067K
Citation: Faraday Discuss., 2018, Advance Article
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    Evolutionary niching in the GAtor genetic algorithm for molecular crystal structure prediction

    F. Curtis, T. Rose and N. Marom, Faraday Discuss., 2018, Advance Article , DOI: 10.1039/C8FD00067K

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