Managing Data Spaces, Performing MD, and Analyzing Trajectories with Signac, HOOMD-Blue, and Freud

We demonstrate how to efficiently develop a complete, integrated, and reproducible molecular-dynamics workflow covering everything from the setup of a parameter space to the execution of simulations and the post-processing of output data for analysis and visualization. Participants are introduced to the basics of managing data spaces with signac [1]. That includes the management and searching of data and metadata. We show how to setup and execute restartable molecular-dynamics simulations with the GPU-accelerated particle simulation toolkit HOOMD-blue [2] with emphasis on the definition of initial configurations, boundary conditions, and simulation protocols. We present the analysis of trajectory data using the parallelized high-performance analysis code freud [3]. The individual steps will be integrated with signac-flow, which allows us to chain and execute data space operations and easily submit workflows to high-performance clusters.

All tutorial material is presented in the form of published Python Jupyter Notebooks. This enables participants to easily follow all examples, and follow-up on the presented workflows.

[1] https://glotzerlab.engin.umich.edu/signac
[2] https://glotzerlab.engin.umich.edu/hoomd-blue/
[3] https://glotzerlab.engin.umich.edu/freud/