GAtor: A First-Principles Genetic Algorithm for Molecular Crystal Structure Prediction

We present the implementation of
GAtor, a massively parallel, first-principles
genetic algorithm (GA) for molecular crystal structure prediction.
GAtor is written in Python and currently interfaces with the FHI-aims
code to perform local optimizations and energy evaluations using dispersion-inclusive
density functional theory (DFT). GAtor offers a variety of fitness
evaluation, selection, crossover, and mutation schemes. Breeding operators
designed specifically for molecular crystals provide a balance between
exploration and exploitation. Evolutionary niching is implemented
in GAtor by using machine learning to cluster the dynamically updated
population by structural similarity and then employing a cluster-based
fitness function. Evolutionary niching promotes uniform sampling of
the potential energy surface by evolving several subpopulations, which
helps overcome initial pool biases and selection biases (genetic drift).
The various settings offered by GAtor increase the likelihood of locating
numerous low-energy minima, including those located in disconnected,
hard to reach regions of the potential energy landscape. The best
structures generated are re-relaxed and re-ranked using a hierarchy
of increasingly accurate DFT functionals and dispersion methods. GAtor
is applied to a chemically diverse set of four past blind test targets,
characterized by different types of intermolecular interactions. The
experimentally observed structures and other low-energy structures
are found for all four targets. In particular, for Target II, 5-cyano-3-hydroxythiophene,
the top ranked putative crystal structure is a Z′
= 2 structure with P1̅ symmetry and a scaffold
packing motif, which has not been reported previously.