Genarris 3.0: Generating Close-Packed Molecular Crystal Structures with Rigid Press

Polymorphism in molecular crystals influences their properties and performance. Crystal structure prediction (CSP) can help explore the crystal structure landscape and discover potentially stable polymorphs. We present a new version of the Genarris open-source code, which generates random molecular crystal structures in all space groups and applies physical constraints on intermolecular distances. The main new feature in Genarris 3.0 is the ``Rigid Press" algorithm, which uses a regularized hard-sphere potential to compress the unit cell and achieve a close-packed structure based on geometric considerations without performing any energy evaluations. Genarris 3.0 is interfaced with machine-learned interatomic potentials (MLIPs) to accelerate the exploration of the potential energy landscape. Our new clustering and down-selection workflow employs the MACE-OFF MLIPs to perform geometry relxation and energy ranking in the early stages. We use Genarris 3.0 to successfully predict the structure of six compounds. We analyze the performance of MACE-OFF compared to dispersion-inclusive density functional theory (DFT) for geometry relaxation and energy ranking. The performance of MACE-OFF across chemically diverse targets is variable, especially for energetic materials. This is mitigated by our clustering and down-selection procedure. Genarris 3.0 can be used effectively to perform CSP and to generate molecular crystal datasets for training ML models. DOI: 10.26434/chemrxiv-2025-046zn