Citation¶
If you use Genarris in your research, please cite the relevant papers below.
Genarris 3.0¶
Yang, Y., Tom, R., Wui, J. A., Moussa, J. E., & Marom, N. Genarris 3.0: Generating Close-Packed Molecular Crystal Structures with Rigid Press. Journal of Chemical Theory and Computation, 21, 11318–11332 (2025).
@article{genarrisv3,
title = {Genarris 3.0: Generating Close-Packed Molecular Crystal Structures with Rigid Press},
author = {Yang, Yi and Tom, Rithwik and Wui, Jose AGL and Moussa, Jonathan E and Marom, Noa},
journal = {Journal of Chemical Theory and Computation},
volume = {21},
number = {21},
pages = {11318--11332},
year = {2025},
publisher = {ACS Publications}
}
Genarris 2.0¶
Tom, R., Rose, T., Bier, I., O’Brien, H., Vázquez-Mayagoitia, Á., & Marom, N. Genarris 2.0: A random structure generator for molecular crystals. Computer Physics Communications, 250, 107170 (2020).
@article{genarrisv2,
title = {Genarris 2.0: A random structure generator for molecular crystals},
author = {Tom, Rithwik and Rose, Timothy and Bier, Imanuel and O'Brien, Harriet
and V{\'a}zquez-Mayagoitia, {\'A}lvaro and Marom, Noa},
journal = {Computer Physics Communications},
volume = {250},
pages = {107170},
year = {2020},
publisher = {Elsevier}
}
Genarris 1.0¶
Li, X., Curtis, F. S., Rose, T., Schober, C., Vazquez-Mayagoitia, A., Reuter, K., Oberhofer, H., & Marom, N. Genarris: Random generation of molecular crystal structures and fast screening with a Harris approximation. The Journal of Chemical Physics, 148, 24 (2018).
@article{genarrisv1,
title = {Genarris: Random generation of molecular crystal structures and fast screening
with a Harris approximation},
author = {Li, Xiayue and Curtis, Farren S and Rose, Timothy and Schober, Christoph
and Vazquez-Mayagoitia, Alvaro and Reuter, Karsten and Oberhofer, Harald
and Marom, Noa},
journal = {The Journal of Chemical Physics},
volume = {148},
number = {24},
year = {2018},
publisher = {AIP Publishing}
}
PyMoVE¶
Bier, I. & Marom, N. Machine learned model for solid form volume estimation based on packing-accessible surface and molecular topological fragments. The Journal of Physical Chemistry A, 124, 10330-10345 (2020).
@article{pymove,
title = {Machine learned model for solid form volume estimation based on
packing-accessible surface and molecular topological fragments},
author = {Bier, Imanuel and Marom, Noa},
journal = {The Journal of Physical Chemistry A},
volume = {124},
number = {49},
pages = {10330--10345},
year = {2020},
publisher = {ACS Publications}
}