GAtor

GAtor

A genetic algorithm (GA) for structure prediction of molecular crystals

A genetic algorithm performs global optimization by mimicking an evolutionary process. The property being optimized is mapped onto a fitness function and structures with a higher fitness are assigned a higher probability for mating. Crossover and mutation operators generate offspring by combining or altering the structural genes of parent structures, such that structural features associated with a high fitness are propagated in the population. GAtor performs structure prediction for crystals of (semi-)rigid molecules with no internal rotational degrees of freedom. It is written in Python and distributed under a BSD-3 license. GAtor interfaces with the FHI-aims electronic structure package for energy evaluations and geometry relaxations with dispersion-inclusive density functional theory (DFT). It is recommended to start GAtor from a diverse initial pool of structures generated by Genarris.

  • Random structures are generated in all (or user-defined) space groups to exhaustively sample the potential energy surface.
  • Physically motivated constraints are imposed on intermolecular distances to ensure the quality of the structures.
  • Fast energy evaluations are performed with the Harris approximation.
  • Machine learning is used for clustering based on a relative coordinate descriptor (RCD) developed specifically for molecular crystals.
  • Standard and user-defined workflows comprising different sequences of energy evaluation, clustering, and selection steps produce curated sets of structures to serve as initial populations for global optimization algorithms and/or as training sets for machine learning.