A simple, fast, and powerful Python library designed for Molecular Dynamics (MD) simulation data analysis! 💪
mdapy provides a comprehensive suite of powerful, flexible, and straightforward tools for analyzing atomic trajectories generated from Molecular Dynamics (MD) simulations.
- ✨ Cross-Platform Support
- Fully compatible with Windows, Linux, and Mac OS - run anywhere seamlessly
- ⚡ Blazing Fast Performance
- Accelerated by C++ using Nanobind
- Highly parallelized, fully utilizing multicore CPU resources
- Lightning-fast file I/O for large datasets
- 🔧 Comprehensive Format Support
Native support for mainstream MD formats:
- LAMMPS (DUMP/DATA)
- VASP (POSCAR)
- Universal XYZ format
- Custom MP format
- 🔗 Seamless Integration
- All data stored in Polars DataFrame and transfer with NumPy NDARRAY format
- Easy integration with Python scientific ecosystem
- Perfect compatibility with post-processing tools like OVITO, ASE and freud
- Fixed cutoff distance neighbor
- Fixed number nearest neighbor
- Voronoi neighbor
- Ackland Jones Analysis
- Common Neighbor Parameter
- CentroSymmetry Parameter
- Common Neighbor Analysis
- Polyhedral Template Matching
- Identify Fcc Planar Faults
- Identify Diamond Structure
- Structure Entropy
- AtomicStrain
- Steinhardt Bond Orientation
- Radial Distribution Function
- Bond Analysis
- Angular Distribution Function
- Structure Factor
- Large scale polycrystalline structure generation
- Standard lattice configuration creation
- High entropy alloy structure
- NEP potential support
- Elastic constant
- Minimization with cell optimization
- Equation of state
- Stacking faults energy
- Phonon dispersion
- Generate EAM potential
- Average EAM potential
- Mean Square Displacement
- Lindemann Parameter
- Cluster Analysis
- Void Analysis
- Warren Cowley Parameter
- Average Atomic Temperature
- Average by Neighbor
- Atomic Voronoi Volume
- Multi-dimensional Spatial Binning
- Parallelly Compress file to .gz format
Method 1: Install via pip (Recommended)
pip install mdapyMethod 2: Install with all optional features
# Install with matplotlib
pip install mdapy[plot]# Install with k3d
pip install mdapy[k3d]# Install all optional packages (matplotlib, k3d, phonopy, pyfftw), except lammps
pip install mdapy[all]Method 3: Install from source
git clone https://github.com/mushroomfire/mdapy.git
cd mdapy
pip install .Method 4: Install directly from GitHub
pip install https://github.com/mushroomfire/mdapy/archive/master.zip --force-reinstall- Python 3.8+
- C++ compilation environment (C++17 or newer)
- OpenMP support
- Tested platforms: Windows 10 (MSVC), Ubuntu (GCC), Mac OS M1 (Clang)
python -c "import mdapy as mp; print('mdapy version is:', mp.__version__)"🎬 Basic Usage
import mdapy as mp
# Load LAMMPS DUMP file
system = mp.System('your_file.dump')
# View system information
print(system)
# Calculate radial distribution function
rdf = system.cal_radial_distribution_function(rc=5.0)
rdf.plot()
# Identify crystal structures
system.cal_polyhedral_template_matching()- 📖 Full Documentation
- https://mdapy.readthedocs.io/
- 🎓 Tutorial Repository
- https://github.com/mushroomfire/mdapy-tutorial
- 💬 Issue Tracker
- https://github.com/mushroomfire/mdapy/issues
- 🏠 Project Homepage
- https://github.com/mushroomfire/mdapy
Core Dependencies
| Package | Version | Purpose |
|---|---|---|
| numpy | latest | Array computations |
| polars | latest | Data processing |
Optional Dependencies
| Package | Version | Purpose |
|---|---|---|
| matplotlib | latest | Data visualization |
| k3d | latest | Atom 3D visualization |
| pyfftw | latest | Fast Fourier Transform |
| phonopy | latest | Phonon calculations |
| lammps | latest | Potential calculations |
We have undergone a major upgrade from mdapy 1.0 (almost rewriting everyting). The old version of mdapy (≤0.11.5) can be found in this branch.
If you use mdapy in your scientific publications, please cite the following paper:
@article{mdapy2023,
title = {mdapy: A flexible and efficient analysis software for molecular dynamics simulations},
journal = {Computer Physics Communications},
pages = {108764},
year = {2023},
issn = {0010-4655},
doi = {https://doi.org/10.1016/j.cpc.2023.108764},
url = {https://www.sciencedirect.com/science/article/pii/S0010465523001091},
author = {Yong-Chao Wu and Jian-Li Shao},
keywords = {Simulation analysis, Molecular dynamics, Polycrystal, TaiChi, Parallel computing}
}If you find mdapy useful, please give us a Star ⭐!
Your support is our motivation for continuous improvement! 🙏
This project is licensed under the BSD 3-Clause License - see the LICENSE file for details.
Issues and Pull Requests are welcome!
Let's make mdapy better together! 💪
If you have any questions or suggestions, feel free to reach us through:
- 📬 Submit an Issue: https://github.com/mushroomfire/mdapy/issues
- 💬 Email to Author: 934313174@qq.com