cp2k / cp2k
git clone https://github.com/cp2k/cp2k.git
v2026.2 · 3mo ago
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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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Star history (as recorded here)
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 ★
- 2026-10-23 — #5 monthly in fortran · +19 ★
- 2026-10-22 — #5 daily in fortran · +1 ★
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- 2026-10-20 — #6 daily in fortran
- 2026-10-20 — #6 monthly in fortran · +17 ★
GitHub topics
Links
- GitHub: github.com/cp2k/cp2k
- Homepage: https://www.cp2k.org
- Releases · Issues · Hacker News mentions
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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