{"categories":[{"label":"Libraries","url":"https://skillfed.io/packages/category/software-development-libraries/6"},{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/4"},{"label":"Python Modules","url":"https://skillfed.io/packages/category/software-development-libraries-python-modules/13"},{"label":"Bio-Informatics","url":"https://skillfed.io/packages/category/scientific-engineering-bio-informatics"},{"label":"Physics","url":"https://skillfed.io/packages/category/scientific-engineering-physics"},{"label":"Chemistry","url":"https://skillfed.io/packages/category/scientific-engineering-chemistry"}],"enrichment":{"capability":"Vesin computes neighbor lists for atomistic systems\u2014identifying which atoms are within a cutoff distance of each other\u2014with a Python interface backed by compiled code for speed.","skillfed_tags":["molecular-dynamics","computational-chemistry","neighbor-search"],"use_cases":["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."],"what_it_does":"Vesin is a compiled library for computing neighbor lists in atomistic simulations\u2014the 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.\n\nYou 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.","worth_installing":"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."},"id":"vesin","links":{"html":"https://skillfed.io/packages/vesin","md":"https://skillfed.io/packages/vesin.md","pypi":"https://pypi.org/project/vesin/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-07-29","license_spdx":"BSD-3-Clause","license_treatment":"permissive","name":"vesin","python_support":"supports_current","summary":"Computing neighbor lists for atomistic system"},"popularity":{"monthly_downloads":351679,"position":7322,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.6.1"}