Genarris 2.0: A Random Structure Generator for Molecular Crystals

Genarris 2.0 is an open-source Python code, parallilized with mpi4py, that performs configuration space screening of molecular crystals by random structure generation. It may be used for generating initial populations to seed other structure search algorithms (such as genetic algorithms) or for generating datasets to train machine learning models. The target unit cell volume is estimated from the single molecule structure by a machine-learned model trained on data from the Cambridge Structural Database (CSD). Crystal structures are then generated in all space groups compatible with the requested number of molecules per cell (Z) with one molecule in the asymmetric unit (Z’=1), including those with special Wyckoff positions. To avoid unphysically close intermolecular distances, structures undergo a cascade of three increasingly rigorous checks. Special settings are applied for strong hydrogen bonds, which are automatically detected. Once an initial dataset of several thousand structures is generated, a smaller dataset may be selected based on quality and diversity criteria via user-defined workflows. For clustering Genarris uses the affinity propagation machine learning algorithm with a relative coordinate descriptor (RCD) or a radial symmetry function (RSF) representation.