Evolutionary Niching in the GAtor Genetic Algorithm for Molecular Crystal Structure Prediction
Abstract
The goal of molecular crystal structure prediction is to find all plausible polymorphs for a given molecule. This requires performing global optimization over a high dimensional search space. Genetic algorithms (GAs) perform global optimization by mimicking evolution. New structures are generated 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 machine learning to dynamically cluster the population by 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. Using evolutionary niching increases the success rate of generating the experimental structure of 1,3-dibromo-2-chloro-5-fluorobenzene and additional low-energy structures with similar packing motifs.
- Publication:
-
APS March Meeting Abstracts
- Pub Date:
- 2019
- Bibcode:
- 2019APS..MARH16003R