Genarris 2.0: A Random Structure Generator for Molecular Crystals

TL;DR

Genarris 2.0 is an enhanced open-source Python tool for generating random molecular crystal structures, incorporating parallelization, machine learning-based volume estimation, and advanced structure validation to aid crystal prediction and machine learning training.

Contribution

The paper introduces major improvements in Genarris, including parallelization, a machine learning volume estimator, and new algorithms for structure generation and validation.

Findings

Successfully generated structures for benzene and glycine

Final pools contained experimental or similar structures

Enhanced efficiency and accuracy in structure validation

Abstract

Genarris is an open-source Python package for generating random molecular crystal structures with physical constraints for seeding crystal structure prediction algorithms and training machine learning models. Here we present a new version of the code, containing several major improvements. An MPI-based parallelization scheme has been implemented, which facilitates the seamless sequential execution of user-defined workflows. A new method for estimating the unit cell volume based on the single molecule structure has been developed using a machine-learned model trained on experimental structures. A new algorithm has been implemented for generating crystal structures with molecules occupying special Wyckoff positions. A new hierarchical structure check procedure has been developed to detect unphysical close contacts efficiently and accurately. New intermolecular distance settings have been implemented for strong hydrogen bonds. To demonstrate these new features, we study two specific cases: benzene and glycine. For all polymorphs, the final pool either contained the experimental structure, or structures with similar lattice parameters, symmetry, and packing motifs.