The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
HOOMD-blue: A Python package for high-performance molecular dynamics and hard particle Monte Carlo simulations
Open accessAbstract
HOOMD-blue is a particle simulation engine designed for nano- and colloidal-scale molecular dynamics and hard particle Monte Carlo simulations. It has been actively developed since March 2007 and available open source since August 2008. HOOMD-blue is a Python package with a high performance C++/CUDA backend that we built from the ground up for GPU acceleration. The Python interface allows users to combine HOOMD-blue with with other packages in the Python ecosystem to create simulation and analysis workflows. We employ software engineering practices to develop, test, maintain, and expand the code.
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Cites
Fast Parallel Algorithms for Short-Range Molecular Dynamics
Plimpton, S. (1995) · Journal of Computational Physics · doi:10.1006/jcph.1995.1039 · Cited by 44,721
Scalable molecular dynamics with NAMD
Phillips, J. C., Braun, R., Wang, W., Gumbart, J., Tajkhorshid, E., Villa, E., Chipot, C., Skeel, R. D., Kalé, L., & Schulten, K. (2005) · Journal of Computational Chemistry · doi:10.1002/jcc.20289 · Cited by 16,914
Singularity: Scientific containers for mobility of compute
Kurtzer, G. M., Sochat, V., & Bauer, M. W. (2017) · PLOS ONE · doi:10.1371/journal.pone.0177459 · Cited by 2,740
Cited by
Thompson, A. P., Aktulga, H. M., Berger, R., Bolintineanu, D. S., Brown, W. M., Crozier, P. S., in 't Veld, P. J., Kohlmeyer, A., Moore, S. G., Nguyen, T. D., et al. (2022) · Computer Physics Communications · doi:10.1016/j.cpc.2021.108171 · Cited by 12,598
Scaling deep learning for materials discovery
Merchant, A., Batzner, S., Schoenholz, S. S., Aykol, M., Cheon, G., & Cubuk, E. D. (2023) · Nature · doi:10.1038/s41586-023-06735-9 · Cited by 1,527
An in-depth review of AI-powered advancements in cancer drug discovery
Le, M. H. N., Nguyen, P. K., Nguyen, T. P. T., Nguyen, H. Q., Tam, D. N. H., Huynh, H. H., Huynh, P. K., & Le, N. Q. K. (2025) · Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease · doi:10.1016/j.bbadis.2025.167680 · Cited by 15
A computational toolbox for the assembly yield of complex and heterogeneous structures
Curatolo, A. I., Kimchi, O., Goodrich, C. P., Krueger, R. K., & Brenner, M. P. (2023) · Nature Communications · doi:10.1038/s41467-023-43168-4 · Cited by 15
ToPolyAgent: AI agents for coarse-grained bead-spring topological polymer simulations
Ding, L., Carrillo, J.-M., & Do, C. (2026) · Digital Discovery · doi:10.1039/d5dd00471c · Cited by 11
“Third, integrating LLM-based AI agents with simulation frameworks supporting Monte Carlo methods — such as GOMC [74], SPARKS [75] and HOOMD-blue [76], would further enhance the system’s versatility.”
Metadata
- Authors
- Anderson, J. A., Glaser, J., & Glotzer, S. C.
- Venue
- Computational Materials Science
- Publisher
- Elsevier BV
- Published
- 2020-02
- Volume
- 173
- Pages
- 109363
- ISSN
- 0927-0256 (ISSN-L)
- OpenAlex
- W2980609865
- Cited by
- 719
- References
- 3
- Type
- software paper
- Language
- en
- Open access
- bronze
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