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cp2k / cp2k

git clone https://github.com/cp2k/cp2k.git v2026.2 · 3mo ago 🔥 on GitHub Trending today

Fortran package for atomistic simulations of solids, liquids, and molecules using DFT, semi-empirical, and force-field methods.

Why you might care

Built for computational chemists and materials scientists running molecular dynamics, metadynamics, or transition state searches at scale. It supports GPW/GAPW DFT alongside xTB, MP2, GW, and classical force fields, and parallelizes via MPI, threads, and GPUs (CUDA, HIP, OpenCL).

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1.2k
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forks · 136 open issues

Star history (as recorded here)

2026-10-20 · 1,225 ★ 2026-10-23 · 1,229 ★
LanguageFortran
LicenseGPL-2.0
Created2018-10-02 (8y ago)
Last pushtoday
Watchers32
OwnerOrganization

Seen on GitHub Trending

  • 2026-10-23 — #3 daily in fortran · +1 ★
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GitHub topics

Links

README excerpt
# CP2K
[![Release Status][release-badge]][release-link] [![Debian Status][debian-badge]][debian-link]
[![Fedora Status][fedora-badge]][fedora-link] [![Ubuntu Status][ubuntu-badge]][ubuntu-link]
[![Homebrew Status][homebrew-badge]][homebrew-link] [![Docker Status][docker-badge]][docker-link]
[![Spack Status][spack-badge]][spack-link] [![Conda Status][conda-badge]][conda-link]
CP2K is a quantum chemistry and solid state physics software package that can perform atomistic
simulations of solid state, liquid, molecular, periodic, material, crystal, and biological systems.
CP2K provides a general framework for different modeling methods such as DFT using the mixed
Gaussian and plane waves approaches GPW and GAPW. Supported theory levels include DFT, MP2, RPA, GW,
tight-binding (xTB, DFTB), semi-empirical methods (AM1, PM3, PM6, RM1, MNDO, ...), and classical
force fields (AMBER, CHARMM, ...). CP2K can do simulations of molecular dynamics, metadynamics,
Monte Carlo, Ehrenfest dynamics, vibrational analysis, core level spectroscopy, energy minimization,
and transition state optimization using NEB or dimer method.
CP2K is written in Fortran 2008 and can be run efficiently in parallel using a combination of
multi-threading, MPI, and GPU acceleration (CUDA for NVIDIA GPUs, HIP/ROCm for AMD GPUs, and
OpenCL).
## Downloading CP2K source code
For end users, the preferred method is to go to Releases
and download and extract one of the versioned tarballs `cp2k-X.Y.tar.bz2`.
For developers, the preferred method is to fork the `cp2k/cp2k`
repository and download via `git clone`. To clone the current master (development version):
 [code] 
To clone a release version v*x.y*:
 [code] 
## Install CP2K
The installation instructions in
the CP2K manual provide several methods for installing CP2K with dependencies:
- Build from Source
- Build with Spack
- Install from Distribution
## Links
- CP2K.org for showcases of scientific work, tutorials, exercises,
 presentation slides, etc.
- The manual with descriptions of all the keywords for the CP2K input
 file
- The dashboard to get an overview of the currently tested
 architectures
- The Google group to get help if you could not find an
 answer in one of the previous links
- Acknowledgements for list of institutions and grants that help to
 fund the development of CP2K
## Directory organization
- `src`: The source code
- `data`: Simulation parameters e.g. basis sets and pseudopotentials
- `docs`: The markdown source pages for documentations that is rendered as the manual
- `tests`: Inputs for tests and regression tests
- `tools`: Mixed collection of useful scripts related to CP2K
- `benchmarks`: Inputs for benchmarks
[conda-badge]: https://img.shields.io/conda/vn/conda-forge/cp2k
[conda-link]: https://anaconda.org/conda-forge/cp2k
[debian-badge]: https://img.shields.io/debian/v/cp2k
[debian-link]: https://packages.debian.org/search?keywords=cp2k
[docker-badge]: https://img.shields.io/docker/v/cp2k/cp2k?label=docker
[docker-li

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