Abstract: A crystal structure prediction (CSP) workflow, based on the random structure generator, Genarris,
and the genetic algorithm (GA), GAtor, is applied to the energetic materials 2,4,6-trinitrobenzene-
1,3,5-triamine (TATB) and 2,4,6-trinitrobenzene-1,3-diamine (DATB), and the chiral arene, 4,5-
dimethylphenanthrene. The experimental structures of all three materials are successfully generated
multiple times by both Genarris and GAtor, and ranked as the most stable structures by dispersion-
inclusive DFT methods. For 4,5-dimethylphenanthrene the evolutionary niching feature of GAtor
helps find the experimental structure by penalizing the fitness of over-sampled regions and steering
the GA to an under-explored basin. For DATB, a putative structure with a sheet packing motif,
which is associated with reduced sensitivity, is found to be very close in energy to the experimental
structure and could be a viable polymorph. Principal component analysis of atom-centered symmetry
functions is used to compare the crystal structure landscapes of TATB and DATB. Genarris and
GAtor exhibit robust performance for diverse targets with varied intermolecular interactions. This
work demonstrates the potential of including CSP as a part of the energetic materials development
process.
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