Genarris 2.0: A random structure generator for molecular crystals
Rithwik TomCarnegie Mellon University
Timothy RoseCarnegie Mellon University
Imanuel BierCarnegie Mellon University
Harriet O’BrienCarnegie Mellon University
Álvaro Vázquez-MayagoitiaArgonne National Lab
Noa MaromCarnegie Mellon University
Description of this data
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. A 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. Genarris finds the experimental structures of the two polymorphs of benzene and the three polymorphs of glycine.
Experiment data files
This data is associated with the following publication:
Cite this dataset
Tom, Rithwik; Rose, Timothy; Bier, Imanuel; O’Brien, Harriet; Vázquez-Mayagoitia, Álvaro; Marom, Noa (2020), “Genarris 2.0: A random structure generator for molecular crystals”, Mendeley Data, v1 http://dx.doi.org/10.17632/grx6mz4pjn.1
The files associated with this dataset are licensed under a BSD 3-Clause ("Revised") License licence.