{"categories":[{"label":"Libraries","url":"https://skillfed.io/packages/category/software-development-libraries/7"},{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/5"},{"label":"Python Modules","url":"https://skillfed.io/packages/category/software-development-libraries-python-modules/14"},{"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":"Computes neighbor lists for atomistic systems efficiently, with support for periodic boundary conditions and GPU execution via PyTorch.","skillfed_tags":["molecular-dynamics","gpu-accelerated","chemistry"],"use_cases":["Accelerating molecular dynamics simulations by computing neighbor lists on GPU with torch.","Building machine learning models for atomistic systems that need efficient neighbor pair enumeration.","Computing pairwise distances and indices for large crystal supercells with periodic boundaries.","Prototyping interatomic potential models that require fast neighbor list updates.","Integrating into existing chemistry workflows that need neighbor list performance improvements."],"what_it_does":"Vesin-torch is a library for computing neighbor lists in atomistic simulations\u2014a fundamental operation in molecular dynamics and materials science where you need to identify which atoms are within a cutoff distance of each other. It wraps the Vesin C library and exposes it through PyTorch, enabling both CPU and GPU execution. The package provides a straightforward Python API via the NeighborList class, which takes atomic positions and a cutoff distance, then returns pairs of neighbor indices and their separation vectors, optionally accounting for periodic boundary conditions.\n\nThe library is designed for performance on large systems. It also offers drop-in compatibility with existing workflows. The single runtime dependency is torch, and the package is actively maintained with recent releases and no known security vulnerabilities.","worth_installing":"Yes, if you work with atomistic simulations or machine learning on molecular systems and use torch. The package is actively maintained, has no security vulnerabilities, carries a permissive license, and solves a real performance bottleneck. Medium install friction is acceptable given the platform-specific wheels and single torch dependency. Not necessary if you already have adequate neighbor list performance or don't use torch."},"id":"vesin-torch","links":{"html":"https://skillfed.io/packages/vesin-torch","md":"https://skillfed.io/packages/vesin-torch.md","pypi":"https://pypi.org/project/vesin-torch/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-07-29","license_spdx":"BSD-3-Clause","license_treatment":"permissive","name":"vesin-torch","python_support":"supports_current","summary":"Computing neighbor lists for atomistic system, in TorchScript"},"popularity":{"monthly_downloads":263606,"position":8353,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.6.1"}