Bradley D. Dice
Department of Physics, University of Michigan, Ann Arbor

Vyas Ramasubramani
Department of Chemical Engineering, University of Michigan, Ann Arbor

Eric S. Harper
Department of Materials Science \& Engineering, University of Michigan, Ann Arbor

Matthew P. Spellings
Department of Chemical Engineering, University of Michigan, Ann Arbor

Joshua A. Anderson
Department of Chemical Engineering, University of Michigan, Ann Arbor

Sharon C. Glotzer
Department of Physics, University of Michigan, Ann Arbor
Department of Chemical Engineering, University of Michigan, Ann Arbor
Department of Materials Science and Engineering, University of Michigan, Ann Arbor
Biointerfaces Institute, University of Michigan, Ann Arbor

Abstract

The freud Python library analyzes particle data output from molecular dynamics simulations. The library's design and its variety of high-performance methods make it a powerful tool for many modern applications. In particular, freud can be used as part of the data generation pipeline for machine learning (ML) algorithms for analyzing particle simulations, and it can be easily integrated with various simulation visualization tools for simultaneous visualization and real-time analysis. Here, we present numerous examples both of using freud to analyze nano-scale particle systems by coupling traditional simulational analyses to machine learning libraries and of visualizing per-particle quantities calculated by freud analysis methods. We include code and examples of this visualization, showing that in general the introduction of freud into existing ML and visualization workflows is smooth and unintrusive. We demonstrate that among Python packages used in the computational molecular sciences, freud offers a unique set of analysis methods with efficient computations and seamless coupling into powerful data analysis pipelines.

Keywords

molecular dynamics, analysis, particle simulation, particle system, computational physics, computational chemistry

DOI

10.25080/Majora-7ddc1dd1-004

Bibtex entry

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Copyright © 2019 Bradley D. Dice et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.