# zelll: a fast, framework-free, and flexible implementation of the cell lists algorithm for the Rust programming language

**Authors:** Vincent Messow, Christian Höner zu Siederdissen, Michael Habeck

PMC · DOI: 10.1093/bioadv/vbaf330 · Bioinformatics Advances · 2026-01-02

## TL;DR

zelll is a fast and flexible Rust library implementing the cell lists algorithm for efficiently computing particle interactions in simulations.

## Contribution

zelll introduces a sparse storage variant of the cell lists algorithm suitable for dynamic and sparsely populated simulations.

## Key findings

- zelll's sparse grid approach enables efficient pairwise interaction calculations in non-traditional simulation setups.
- The library is designed for flexibility and does not target specific molecular dynamics frameworks.
- Python bindings are available, expanding its usability beyond Rust.

## Abstract

The cell lists algorithm is widely used to compute pairwise particle interactions below a fixed cutoff distance in approximately linear time. Prominent molecular dynamics frameworks implementing cell lists variants assume pre-determined and densely populated simulation boxes suitable for e.g. all-atom simulations with explicit solvents. zelll implements a simple yet efficient variant of the cell lists algorithm that uses sparse storage for the underlying partitioning grid. This allows for applications with dynamic simulation boundaries and sparsely populated simulation space not strictly fitting into the scope of common molecular dynamics frameworks, such as many coarse-grained simulations. For this reason, zelll does not target specific frameworks.

zelll is an open-source Rust library available under the MIT license at https://github.com/microscopic-image-analysis/zelll and https://crates.io/crates/zelll. Python bindings are available at https://pypi.org/project/zelll.

## Full text

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## Figures

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## References

23 references — full list in the complete paper: https://tomesphere.com/paper/PMC12910374/full.md

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Source: https://tomesphere.com/paper/PMC12910374