Citing HOOMD-blue¶

Please cite this publication in any work that uses HOOMD-blue:

J. A. Anderson, J. Glaser, and S. C. Glotzer. HOOMD-blue: A Python package for high-performance molecular dynamics and hard particle Monte Carlo simulations. Computational Materials Science 173: 109363, Feb 2020. 10.1016/j.commatsci.2019.109363

The following publications document significant contributions to features in HOOMD-blue. We encourage you to cite these, if possible, when you make use of these specific functionalities.


J. A. Anderson, M. E. Irrgang, and S. C. Glotzer. Scalable Metropolis Monte Carlo for simulation of hard shapes. Computer Physics Communications 204: 21-30, July 2016. 10.1016/j.cpc.2016.02.024

Implicit depletants in HPMC:

J. Glaser, A. S. Karas, and S. C. Glotzer. A parallel algorithm for implicit depletant simulations. The Journal of Chemical Physics 143: 184110, 2015. 10.1063/1.4935175

MPI scaling:

J. Glaser, T. D. Nguyen, J. A. Anderson, P. Lui, F. Spiga, J. A. Millan, D. C. Morse, S. C. Glotzer. Strong scaling of general-purpose molecular dynamics simulations on GPUs. Computer Physics Communications 192: 97-107, July 2015. 10.1016/j.cpc.2015.02.028

Intra-node scaling on multiple GPUs:

J. Glaser, P. S. Schwendeman, J. A. Anderson, S. C. Glotzer. Unified memory in HOOMD-blue improves node-level strong scaling. Computational Materials Science 173: 109359, Feb 2020. 10.1016/j.commatsci.2019.109359

Alchemical MD simulations:

G. van Anders, D. Klotsa, A. S. Karas, P. M. Dodd, S. C. Glotzer. Digital Alchemy for Materials Design: Colloids and Beyond. ACS Nano 2015, 9, 10, 9542-9553 10.1021/acsnano.5b04181

P. Zhou, J. C. Proctor, G. van Anders, S. C. Glotzer. Alchemical molecular dynamics for inverse design. Molecular Physics, 117:23-24, 3968-3980 2019. 10.1080/00268976.2019.1680886

Alchemical HPMC simulations:

G. van Anders, D. Klotsa, A. S. Karas, P. M. Dodd, S. C. Glotzer. Digital Alchemy for Materials Design: Colloids and Beyond. ACS Nano 2015, 9, 10, 9542-9553 10.1021/acsnano.5b04181

Y. Geng, G. van Anders, P. M. Dodd, J. Dshemuchadse, S. C. Glotzer. Engineering entropy for the inverse design of colloidal crystals from hard shapes. Science Advances 2019, 5, 7, eaaw051 10.1126/sciadv.aaw0514

When including historical development of HOOMD-blue, or noting that HOOMD-blue was first implemented on GPUs, please also cite:

J. A. Anderson, C. D. Lorenz, and A. Travesset. General purpose molecular dynamics simulations fully implemented on graphics processing units. Journal of Computational Physics 227(10): 5342-5359, May 2008. 10.1016/


M. Spellings, R. L. Marson, J. A. Anderson, and S. C. Glotzer. GPU accelerated Discrete Element Method (DEM) molecular dynamics for conservative, faceted particle simulations. Journal of Computational Physics 334: 460-467, Apr 2017. 10.1016/

The tree or stencil MD neighbor list:

M. P. Howard, J. A. Anderson, A. Nikoubashman, S. C. Glotzer, and A. Z. Panagiotopoulos. Efficient neighbor list calculation for molecular simulation of colloidal systems using graphics processing units. Computer Physics Communications 203: 45-52, Mar 2016. 10.1016/j.cpc.2016.02.003

M. P. Howard, A. Statt, F. Madutsa, T. M. Truskett, and A. Z. Panagiotopoulos. Quantized bounding volume hierarchies for neighbor search in molecular simulations on graphics processing units. Computational Materials Science 164(15): 139-146, June 2019. 10.1016/j.commatsci.2019.04.004


M. P. Howard, A. Z. Panagiotopoulos, and A. Nikoubashman. Efficient mesoscale hydrodynamics: Multiparticle collision dynamics with massively parallel GPU acceleration Computer Physics Communications 230: 10-20, Sep. 2018. 10.1016/j.cpc.2018.04.009

Rigid bodies in MD:

T. D. Nguyen, C. L. Phillips, J. A. Anderson, and S. C. Glotzer. Rigid body constraints realized in massively-parallel molecular dynamics on graphics processing units. Computer Physics Communications 182(11): 2313-2307, June 2011. 10.1016/j.cpc.2011.06.005

J. Glaser, X. Zha, J. A. Anderson, S. C. Glotzer, A. Travesset. Pressure in rigid body molecular dynamics. Computational Materials Science Computational Materials Science 173: 109430, Feb 2020. 10.1016/j.commatsci.2019.109430


C. L. Phillips, J. A. Anderson, and S. C. Glotzer. Pseudo-random number generation for Brownian Dynamics and Dissipative Particle Dynamics simulations on GPU devices Journal of Computational Physics 230(19): 7191-7201, Aug. 2011. 10.1016/


I.V. Morozov, A.M. Kazennova, R.G. Bystryia, G.E. Normana, V.V. Pisareva, and V.V. Stegailova. Molecular dynamics simulations of the relaxation processes in the condensed matter on GPUs. Computer Physics Communications 182(9): 1974-1978, 2011. 10.1016/j.cpc.2010.12.026

L. Yang, F. Zhang, C. Wang, K. Ho, and A. Travesset. Implementation of metal-friendly EAM/FS-type semi-empirical potentials in HOOMD-blue: A GPU-accelerated molecular dynamics software. Journal of Computational Physics 359(15): 352-360, 2018. 10.1016/


D. N. LeBard, B. G. Levine, P. Mertmann, S. A. Barr, A. Jusufi, S. Sanders, M. L. Klein, and A. Z. Panagiotopoulos. Self-assembly of coarse-grained ionic surfactants accelerated by graphics processing units. Soft Matter 8: 2385-2397, 2012. 10.1039/c1sm06787g

CGCMM potential:

B. G. Levine, D. N. LeBard, R. DeVane, W. Shinoda, A. Kohlmeyer, and M. L. Klein. Micellization studied by GPU-accelerated coarse-grained molecular dynamics. Journal of Chemical Theory and Computation 7(12): 4135-4145, Oct. 2011. 10.1021/ct2005193