High-performance molecular dynamics and electronic structure simulations for materials science.
CP2K is a world-class, open-source molecular dynamics and electronic structure software package designed for high-performance computing (HPC) environments. At its core, CP2K utilizes the Gaussian and Plane Waves (GPW) method, which provides a highly efficient and scalable approach to Density Functional Theory (DFT) calculations. As of 2026, it remains a dominant force in the materials science and chemical research sectors due to its ability to handle large systems with linear scaling (LS-DFT) and its robust support for heterogeneous computing architectures, including GPU acceleration via CUDA and ROCm. The software architecture is primarily written in Fortran 2008, designed for massive parallelism using MPI, OpenMP, and specialized libraries like ScaLAPACK and LIBXSMM. CP2K excels in simulating complex systems, from solid-state physics and liquid-phase chemistry to biological macromolecules. Its market position is solidified by its integration into automated AI discovery workflows, where it serves as the ground-truth engine for training graph neural networks (GNNs) and other machine-learned interatomic potentials (MLIPs).
A dual-basis approach using Gaussian functions for orbitals and a plane-wave basis for density, enabling efficient FFT-based Poisson solvers.
The industry-standard engine for massively parallel molecular dynamics and AI-driven materials discovery.
Advanced Ab Initio Quantum Chemistry Software for High-Accuracy Molecular Calculations
The industry-standard Python library for high-performance molecular dynamics trajectory analysis.
The Python bridge for high-throughput molecular dynamics analysis and ML-driven discovery.
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Implementation of the density matrix purification method to achieve O(N) scaling for insulating systems.
Native integration of Quantum Mechanical and Molecular Mechanical force fields with flexible coupling schemes.
Distributed Block Compressed Sparse Row library optimized for small matrix multiplications.
Support for Collective Variables (CVs) and biasing potentials to explore rare events in chemical reactions.
Implementation of GAPW for calculating core-level excitations and spectra.
Native support for DeepMD-kit and integration with training loops for neural network potentials.
Understanding ion transport mechanisms and degradation at the electrode-electrolyte interface.
Registry Updated:2/7/2026
Extract diffusion coefficients from the resulting trajectory files.
Predicting the reaction pathway for CO2 reduction on a catalytic surface.
Modeling the electronic properties of doped silicon surfaces for photovoltaics.