Genarris: Random Generation of Molecular Crystal Structures and Fast Screening with a Harris Approximation

We present Genarris, a Python package that performs configuration space screening for molecular crystals of rigid molecules by random sampling with physical constraints. For fast total energy evaluations Genarris employs a Harris approximation, whereby the total density of a molecular crystal is constructed via superposition of single molecule densities. Dispersion-inclusive density functional theory (DFT) is then used to evaluate the total energy of the Harris density without performing a self-consistency cycle. Genarris uses machine learning for clustering, based on a relative coordinate descriptor (RCD) developed specifically for molecular crystals, which we find to be robust in identifying packing motif similarity. Genarris offers three workflows to screen a raw pool of random structures via different sequences of successive clustering and filtering steps: the “Rigorous” workflow is an exhaustive exploration of the potential energy landscape, the “Energy” workflow produces a set of low energy structures, and the “Diverse” workflow produces a maximally diverse set of structures. The usage of Genarris is demonstrated for three test cases of past blind test targets.