lammps
unoffical LAMMPS Molecular Dynamics Python package
Decision gist · record as of 2026-08-14
Yes, if you need LAMMPS for molecular dynamics work and want to avoid source compilation. The package is actively maintained, has no known vulnerabilities, and provides convenient pre-built wheels across major platforms. However, verify that the GPLv2 copyleft license is compatible with your project's licensing requirements, and confirm that the wheel's MPI version (MPICH or Microsoft MPI) matches your system setup.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Linux requires glibc >= 2.28; macOS requires >= macOS-11.
- MPI support requires matching MPI version (MPICH on Linux/macOS, Microsoft MPI on Windows).
- Medium install friction due to pre-compiled wheels for multiple platforms (Linux x86_64/aarch64, macOS x86_64/arm64, Windows amd64), but requires glibc >= 2.28 on Linux and macOS >= 11.
License · maintenance · safety
copyleft license (copyleft) — Distributed under GNU General Public License v2 (GPLv2), a copyleft license. Any derivative work or modification must be distributed under the same license terms, and source code must be made available to recipients.
last release 2026-04-19 (117 days) · last repo commit 2026-08-14 · 3,017 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 83,739 downloads/mo, #14,050 on PyPI
Alternatives
Verify before relying
pip install lammps
# or with MPI support:
# pip install lammps[mpi]
from lammps import PyLammps
lmp = PyLammps()
# Now use lmp to run LAMMPS commands- Whether MPI support (MPICH on Linux/macOS, Microsoft MPI on Windows) is required for typical use cases or optional
- Performance characteristics and simulation scale limits compared to native LAMMPS builds
- Whether the package supports all LAMMPS features or if some are disabled in the wheel distribution
What it is and what it does
lammps is an unofficial Python package that distributes pre-compiled wheels of the LAMMPS molecular dynamics engine. It allows researchers and developers to run molecular simulations directly from Python code or via command-line tools without building LAMMPS from source. The package bundles LAMMPS with most packages enabled and provides both a high-level PyLammps API for Python and a command-line interface (lmp). It supports plugin registration through entry points, allowing developers to extend functionality with custom plugins built against the same MPI version.
The package targets modern Python (3.7+) and is available for Linux (glibc >= 2.28), macOS (>= 11), and Windows. MPI support is included but requires matching the MPI version used in the wheel (MPICH on Unix-like systems, Microsoft MPI on Windows). Installation is straightforward via pip, with an optional [mpi] extra for systems without MPI pre-installed.
Use it for
- Run molecular dynamics simulations from Python scripts without compiling LAMMPS from source
- Integrate LAMMPS simulations into larger Python workflows for materials science or chemistry research
- Develop and test custom LAMMPS plugins using the entry-point registration system
- Perform quick exploratory simulations via the PyLammps API for educational or prototyping purposes
- Deploy LAMMPS-based applications across multiple platforms using pre-built wheels
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you need LAMMPS for molecular dynamics work and want to avoid source compilation.
The package is actively maintained, has no known vulnerabilities, and provides convenient pre-built wheels across major platforms. However, verify that the GPLv2 copyleft license is compatible with your project's licensing requirements, and confirm that the wheel's MPI version (MPICH or Microsoft MPI) matches your system setup.
Install
lammps on PyPI
Before you install
Medium install friction due to pre-compiled wheels for multiple platforms (Linux x86_64/aarch64, macOS x86_64/arm64, Windows amd64), but requires glibc >= 2.28 on Linux and macOS >= 11. Repository is actively maintained with recent commits and 3017 stars.
Linux requires glibc >= 2.28; macOS requires >= macOS-11. MPI support requires matching MPI version (MPICH on Linux/macOS, Microsoft MPI on Windows).
License in practice
Distributed under GNU General Public License v2 (GPLv2), a copyleft license. Any derivative work or modification must be distributed under the same license terms, and source code must be made available to recipients.
Quickstart
pip install lammps
# or with MPI support:
# pip install lammps[mpi]
from lammps import PyLammps
lmp = PyLammps()
# Now use lmp to run LAMMPS commands
Verify before relying
- Whether MPI support (MPICH on Linux/macOS, Microsoft MPI on Windows) is required for typical use cases or optional
- Performance characteristics and simulation scale limits compared to native LAMMPS builds
- Whether the package supports all LAMMPS features or if some are disabled in the wheel distribution
Package facts
| License | copyleft license copyleft |
| Python support | Supports the current Python release >=3.7 |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | 1 packageimportlib_metadata |
| Maintenance | Actively maintained 117 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 83,739 / month, #14,050 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableEnvironment :: ConsoleLicense :: OSI Approved :: GNU General Public License v2 (GPLv2)Operating System :: OS IndependentProgramming Language :: Python :: 3 |
Evidence: lammps-2025.7.22.4.0-py2.py3-none-macosx_11_0_arm64.whl; lammps-2025.7.22.4.0-py2.py3-none-macosx_11_0_x86_64.whl; lammps-2025.7.22.4.0-py2.py3-none-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl; lammps-2025.7.22.4.0-py2.py3-none-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl; lammps-2025.7.22.4.0-py2.py3-none-win_amd64.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “lammps python bindings”
- lammpslammps provides pre-built Python bindings to the LAMMPS molecular…
- pyocseAutomates simulation workflows for organic crystal mechanical…
- dpdatadpdata converts and manipulates atomistic simulation data between…
Give your agent the search over MCP, or paste the wish link into any chat.
More Scientific/Engineering packages
NumPy provides an N-dimensional array object and a comprehensive suite of mathematical, linear algebra, Fourier transform, and random number functions for scientific computing in Python.
pandas provides fast, flexible data structures (Series and DataFrame) for loading, cleaning, transforming, and analyzing labeled or relational data in Python.
scipy provides numerical algorithms for mathematics, science, and engineering—including optimization, integration, linear algebra, Fourier transforms, signal and image processing, and ODE solvers—built on numpy arrays.
scikit-learn provides a comprehensive Python library for supervised and unsupervised machine learning, including classification, regression, clustering, dimensionality reduction, and model evaluation tools built on NumPy and SciPy.
Install it if you need to train, evaluate, or deploy supervised or unsupervised learning models.
dill extends Python's pickle module to serialize and deserialize a much wider range of Python objects, including functions, lambdas, classes, and interpreter sessions, to byte streams for storage or network transmission.
Multiprocess is an enhanced fork of Python's standard multiprocessing library that uses dill for better serialization, allowing you to spawn processes with a threading-like API and share complex objects between them.
Install it if you use multiprocessing and encounter pickle serialization limits with lambdas or complex objects.
See also openbabel-wheel · OpenMM · pyocse · rdkit-pypi · dpdata · nodejs-wheel · ase · nodejs-wheel-binaries · rdkit · alchemlyb