(566c) The Molecular Simulation Design Framework (MoSDeF): The Latest Integration with Gomc, Cassandra, and HOOMD-Blue | AIChE

(566c) The Molecular Simulation Design Framework (MoSDeF): The Latest Integration with Gomc, Cassandra, and HOOMD-Blue


Quach, C. D. - Presenter, Vanderbilt University
Craven, N. C., Vanderbilt University
Crawford, B., Wayne State University
DeFever, R. S., Clemson University
Fothergill, J., Boise State University
Iacovella, C., Vanderbilt University
Potoff, J., Wayne State University
Maginn, E., University of Notre Dame
Jankowski, E., Boise State University
McCabe, C., Vanderbilt University
Cummings, P., Vanderbilt University
The Molecular Simulation Design Framework (MoSDeF)[1] was developed as a suite of tools to alleviate issues around initializing and performing molecular simulations with an emphasis on facilitating reproducibility in molecular simulation.[2] By providing standardized, open-source libraries, the MoSDeF can automate steps in the preparation of chemical/biological systems for simulation, and minimize unnecessary human interaction and associated errors. Furthermore, automation of workflows allows for easier setup of high-throughput screening processes, by integrating with workflow management tools such as signac and signac-flow.[3] MoSDeF has been successfully applied to facilitate simulation studies in conjunction with several open source simulation codes. From the beginning, the MoSDeF project has provided support for molecular dynamics simulation engines, such as GROMACS and LAMMPS.[4, 5] As part of the continued community development of MoSDeF support has now been extended to GOMC (via the MoSDeF-GOMC library), Cassandra (via the MoSDeF-Cassandra library), and HOOMD-blue.[6-8] These integrations enable more automated, intuitive, and reproducible ways to set up simulations with these engines. MoSDeF can be used to easily build individual systems for new users and complex/integrated reproducible workflows for experts. This enables these workflows to be used in teaching new users and running training workshops without the usual overhead associated with learning inputs to complex codes. We will continue working to further integrate MoSDeF with other tools/simulation engines existing in the community, joining forces with similar efforts, such as the FAIR consortium and OpenKIM, to effectively address the reproducibility issue of the field.[9, 10]


  1. “MoSDeF” [Online]. Available: https://mosdef.org
  2. Cummings, P. T., MCabe, C., Iacovella, et al. (2021). Open‐source molecular modeling software in chemical engineering focusing on the Molecular Simulation Design Framework. In AIChE Journal (Vol. 67, Issue 3). Wiley. https://doi.org/10.1002/aic.17206
  3. “Signac” [Online]. Available: https://signac.io
  4. Abraham, M. J., Murtola, T., Schulz, R., et al. GROMACS: High Performance Molecular Simulations through Multi-Level Parallelism from Laptops to Supercomputers. SoftwareX, 2015, 1–2, 19–25. https://doi.org/10.1016/j.softx.2015.06.001.
  5. Thompson, A. P., Aktulga, H. M., Berger, R. et al. LAMMPS - a Flexible Simulation Tool for Particle-Based Materials Modeling at the Atomic, Meso, and Continuum Scales. Computer Physics Communications, 2022, 271, 108171. https://doi.org/10.1016/j.cpc.2021.108171.
  6. Crawford, B., Timalsina, U., Quach, C. D., et al. MoSDeF-GOMC: Python Software for the Creation of Scientific Workflows for the Monte Carlo Simulation Engine GOMC. Journal of Chemical Information and Modeling, 2023, 63, 1218–1228. https://doi.org/10.1021/acs.jcim.2c01498.
  7. DeFever, R. S., Matsumoto, R. A., Dowling, A. W., et al. MoSDeF Cassandra: A Complete Python Interface for the Cassandra Monte Carlo Software. Journal of Computational Chemistry, 2021, 42, 1321–1331. https://doi.org/10.1002/jcc.26544.
  8. Anderson, J. A., Glaser, J., Glotzer, S. C. HOOMD-Blue: A Python Package for High-Performance Molecular Dynamics and Hard Particle Monte Carlo Simulations. Computational Materials Science, 2020, 173, 109363. https://doi.org/10.1016/j.commatsci.2019.109363.
  9. Wilkinson, M., Dumontier, M., Aalbersberg, I. et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data 3, 160018 (2016). https://doi.org/10.1038/sdata.2016.18
  10. Tadmor, E. B., Elliott, R. S., Sethna, J. P., Miller, R. E., Becker, C. A. The Potential of Atomistic Simulations and the Knowledgebase of Interatomic Models. JOM, 2011, 63, 17–17. https://doi.org/10.1007/s11837-011-0102-6.