vesin
Computing neighbor lists for atomistic system
Decision gist · record as of 2026-08-14
Yes, if you work with atomistic simulations or machine learning on atomic structures. Vesin solves a specific, performance-critical problem with active maintenance, no known vulnerabilities, and permissive licensing. The medium install friction is offset by precompiled wheels for common platforms and a straightforward API. Not relevant for non-atomistic applications.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python >=3.10 and numpy as a runtime dependency.
- Medium install friction due to precompiled wheels for common platforms (macOS arm64/x86_64, Linux x86_64/aarch64, Windows x86_64), but requires numpy at runtime.
- Repository is active with recent commits and a stable release cadence.
License · maintenance · safety
BSD-3-Clause (permissive) — BSD-3-Clause is permissive; you can use, modify, and distribute vesin freely in commercial or private projects provided you include the license notice and disclaim liability.
last release 2026-07-29 (16 days) · last repo commit 2026-08-07 · 85 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 351,679 downloads/mo, #7,322 on PyPI
Alternatives
Verify before relying
pip install vesin
import numpy as np
from vesin import NeighborList
positions = [(0, 0, 0), (0, 1.3, 1.3)]
box = 3.2 * np.eye(3)
calculator = NeighborList(cutoff=4.2, full_list=True)
i, j, S, d = calculator.compute(points=positions, box=box, periodic=True, quantities="ijSd")- Whether GPU acceleration (mentioned for NVIDIA H100 in benchmarks) is available in the Python interface or only in C/C++.
- Performance characteristics compared to ASE's native neighbor list for typical system sizes.
What it is and what it does
Vesin is a compiled library for computing neighbor lists in atomistic simulations—the core operation of identifying which atoms interact within a specified cutoff distance. It wraps a high-performance C implementation and exposes it via a Python API, with support for periodic boundary conditions and various output formats (pair indices, shift vectors, distances). The library is designed for molecular dynamics, machine learning on atomic structures, and other computational chemistry workflows where neighbor identification is a bottleneck.
You can use it either through its own NeighborList class or via a drop-in replacement for ASE's neighbor list function, making it suitable for integration into existing atomistic simulation pipelines. It requires numpy at runtime and Python 3.10 or later.
Use it for
- Computing pairwise interactions in molecular dynamics simulations with periodic boundary conditions.
- Generating training data for machine learning models on atomic structures by identifying neighbor pairs.
- Accelerating force-field calculations in computational chemistry by quickly finding atoms within cutoff range.
- Integrating into ASE-based workflows as a faster drop-in replacement for neighbor list computation.
- Building graph representations of atomic systems for neural network potentials.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you work with atomistic simulations or machine learning on atomic structures.
Vesin solves a specific, performance-critical problem with active maintenance, no known vulnerabilities, and permissive licensing. The medium install friction is offset by precompiled wheels for common platforms and a straightforward API. Not relevant for non-atomistic applications.
Install
vesin on PyPI
Before you install
Medium install friction due to precompiled wheels for common platforms (macOS arm64/x86_64, Linux x86_64/aarch64, Windows x86_64), but requires numpy at runtime. Repository is active with recent commits and a stable release cadence.
Requires Python >=3.10 and numpy as a runtime dependency.
License in practice
BSD-3-Clause is permissive; you can use, modify, and distribute vesin freely in commercial or private projects provided you include the license notice and disclaim liability.
Quickstart
pip install vesin
import numpy as np
from vesin import NeighborList
positions = [(0, 0, 0), (0, 1.3, 1.3)]
box = 3.2 * np.eye(3)
calculator = NeighborList(cutoff=4.2, full_list=True)
i, j, S, d = calculator.compute(points=positions, box=box, periodic=True, quantities="ijSd")Verify before relying
- Whether GPU acceleration (mentioned for NVIDIA H100 in benchmarks) is available in the Python interface or only in C/C++.
- Performance characteristics compared to ASE's native neighbor list for typical system sizes.
Package facts
| License | BSD-3-Clause permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | 1 packagenumpy |
| Maintenance | Actively maintained 16 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 351,679 / month, #7,322 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaIntended Audience :: Science/ResearchOperating System :: MacOS :: MacOS XOperating System :: Microsoft :: WindowsOperating System :: POSIXProgramming Language :: PythonProgramming Language :: Python :: 3Topic :: Scientific/EngineeringTopic :: Scientific/Engineering :: Bio-InformaticsTopic :: Scientific/Engineering :: ChemistryTopic :: Scientific/Engineering :: PhysicsTopic :: Software Development :: LibrariesTopic :: Software Development :: Libraries :: Python Modules |
Evidence: vesin-0.6.1-py3-none-macosx_11_0_arm64.whl; vesin-0.6.1-py3-none-macosx_11_0_x86_64.whl; vesin-0.6.1-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl; vesin-0.6.1-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl; vesin-0.6.1-py3-none-win_amd64.whl
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