freud: A Software Suite for High Throughput Analysis of Particle Simulation Data
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Abstract (arXiv)
The freud Python package is a powerful library for analyzing simulation data. Written with modern simulation and data analysis workflows in mind, freud provides a Python interface to fast, parallelized C++ routines that run efficiently on laptops, workstations, and supercomputing clusters. The package provides the core tools for finding particle neighbors in periodic systems, and offers a uniform API to a wide variety of methods implemented using these tools. As such, freud users can access standard methods such as the radial distribution function as well as newer, more specialized methods such as the potential of mean force and torque and local crystal environment analysis with equal ease. While many comparable tools place a heavy emphasis on reading and operating on trajectory file formats, freud instead accepts numerical arrays of data directly as inputs. By remaining agnostic to its data source, freud is suitable for analyzing any coarse-grained particle simulation, regardless of the original data representation or simulation method. When used for on-the-fly analysis in conjunction with scriptable simulation software such as HOOMD-blue, freud enables smart simulations that adapt to the current state of the system, allowing users to study phenomena such as nucleation and growth.
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). First posted on arXiv 14 Jun 2019.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 2; 2024: 1; 2025: 0; 2026: 1.
6 papers.
- 2021Geometric Algebra Attention Networks for Small Point Cloudscited by 10
- 2023Neural Schr"odinger Bridge with Sinkhorn Losses: Application to Data-driven Minimum Effort Control of Colloidal Self-assemblycited by 10
- 2022What Do Deep Neural Networks Find in Disordered Structures of Glasses?cited by 2
- 2026ColPackAgent: Agent-Skill-Guided Hard-Particle Monte Carlo Workflows for Colloidal Packingcited by 1
- 2023Classification of complex local environments in systems of particle shapes through shape-symmetry encoded data augmentationcited by 0
- 2024CBR -- Boosting Adaptive Classification By Retrieval of Encrypted Network Traffic with Out-of-distributioncited by 0
- 2026ColPackAgent: Agent-Skill-Guided Hard-Particle Monte Carlo Workflows for Colloidal Packing15 May 2026
- 2024CBR -- Boosting Adaptive Classification By Retrieval of Encrypted Network Traffic with Out-of-distribution17 Mar 2024
- 2023Classification of complex local environments in systems of particle shapes through shape-symmetry encoded data augmentation19 Dec 2023
- 2023Neural Schr"odinger Bridge with Sinkhorn Losses: Application to Data-driven Minimum Effort Control of Colloidal Self-assembly26 Jul 2023
- 2022What Do Deep Neural Networks Find in Disordered Structures of Glasses?31 Jul 2022
- 2021Geometric Algebra Attention Networks for Small Point Clouds5 Oct 2021