mdapy 0.8.7

A simple, fast and cross-platform python library to handle the data generated from molecular dynamics simulations

mdapy : Molecular Dynamics Analysis with Python

Overview

The mdapy python library provides an array of powerful, flexible, and straightforward tools to analyze atomic trajectories generated from Molecular Dynamics (MD) simulations. The library is fully cross-platform, making it accessible to users in Windows, Linux, and Mac OS. Benefited by the TaiChi project, we can effectively accelerate the pure python code, bringing it closer to the speed of code written in C++. Furthermore, mdapy is highly parallelized, allowing users to leverage the resources of both multicore CPU and GPU. mdapy can directly handle the DUMP and DATA formats in LAMMPS. Besides, all data in mdapy is stored in NDARRAY format in NumPy, which enables easy integration with the scientific ecosystem in python and facilitates collaboration with other post-progressing tools such as OVITO and freud.

Resources

• Homepage: https://github.com/mushroomfire/mdapy

• Documentation: https://mdapy.readthedocs.io/

• Issue Tracker: https://github.com/mushroomfire/mdapy/issues

Dependencies

• python (3.7-3.11)

• taichi>=1.6.0

• numpy

• scipy

• pandas

• pyarrow

• matplotlib

Optional Dependencies

• SciencePlots (Optional, for plotting results)

• tqdm (Optional, for progress bar when reading/saving multi DUMP files)

• pyfftw (Optional, for fast FFT)

• pyfnntw>=0.4.1 (Optional, for fast KNN search)

Installation

Install from pip (recommended).

pip install mdapy

Install from source code.

• You should install pybind11 and have a C++ compilation environment (-std=c++11 or newer) and openmp supports. Tested by MSVC in Windows 10, GCC in Ubuntu, Clang in MAC OS M1.

  pip install pybind11

• Download the source code and installation.

  git clone https://github.com/mushroomfire/mdapy.git
  cd mdapy
  pip install .

Usage

import mdapy as mp
mp.init('cpu') # use cpu, mp.init('gpu') will use gpu to compute.

system = mp.System('./example/CoCuFeNiPd-4M.dump') # read dump file to generate a system class
system.cal_centro_symmetry_parameter() # calculate the centrosymmetry parameters
system.cal_atomic_entropy() # calculate the atomic entropy
system.write_dump() # save results to a new dump file

Main Features

1. Structure Analysis

   • Ackland Jones Analysis

   • CentroSymmetry Parameter

   • Common Neighbor Analysis

   • Common Neighbor Parameter

   • Atomic Structure Entropy

   • Steinhardt Bondorder

   • Radiul Distribution Function

   • Polyhedral Template Matching

   • Identify stacking faults (SFs) and twinning boundary (TBs)

2. Potential Analysis

   • Generate EAM/alloy Potential

   • Read EAM/alloy Potential

   • Average EAM/alloy Potential

   • Calculate Atomic Force and Energy by EAM/alloy

3. Melting Analysis

   • Mean Squared Displacement

   • Lindemann Parameter

   • Identify Solid/Liquid Phase

4. Geometry Structure Creation

   • Generate Standard Lattice Structure

   • Generate Polycrystal

5. Neighbor Search

   • Neighbor Atoms within Fixed Distance

   • Neighbor Atoms within Fixed Number

6. Other

   • Void Distribution

   • Cluster Analysis

   • Replication

   • Warren Cowley Parameter

   • Average Atomic Temperature

   • Atomic Voronoi Volume

   • Multi-dimensional Spatial Binning

Citation

If you find mdapy useful, you can star it! If you use mdapy in your scientific publications, please cite the 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}
   }

Trouble Shoot

If you encounter ImportError in Linux:

version 'GLIBCXX_3.4.29' not found.

You can try:

conda install -c conda-forge gxx_linux-64

Release Notes

V0.8.7 (5/25/2023)

• Updated Taichi to 1.6.0, which decreases the import time ans supports Python 3.11.

• Fix bug in read data.

• Updated mdapy citation. We are pleased that our article for mdapy has been accepted by Computer Physics Communications.

V0.8.6 (4/22/2023)

• Add repr for System class.

• Add Replicate class.

• Improve the performance of reading/writing DATA file with pyarrow.

• Improve the performance of building Voronoi diagram with new version voro++.

V0.8.5 (4/9/2023)

• Compile it on MAC OS with M1. Now mdapy is fully cross-platform.

• Obviously improve the performance of reading/writing DUMP with pyarrow.

• Add pyarrow as a dependency package.

• Fix bug of create_polycrystalline module. One can give box with any number, the old version only works for positive float.

• Fix bug of spatial_binning module for empty region.

• Let tqdm as an Optional dependency.

V0.8.4 (3/30/2023)

• Optimize Pair Distribution module.

• Optimize Neighbor module.

• Update many Benchmark cases.

V0.8.3 (3/20/2023)

• Make Polyhedral Template Mathing parallel.

V0.8.2

• Fix bugs of unwrap positions.

• Fix a typo error in msd.

V0.8.1

• Add Steinhardt Bondorder Parameter method, which can be used to identify the lattice structure and distinguish the solid/liquid phase during melting process.

• Add Polyhedral Template Mathing method.

• Add IdentifySFsTBs method to identify the stacking faults (SFs) and twinning boundary (TBs) in FCC lattice.

V0.8.0

• Add Ackland Jones Analysis (AJA) method.

• Add Common Neighbor Parameter (CNP) method.

• Update the nearest neighbor search in CSP method.

V0.7.9

• Fix bug of create_polycrystalline module in Linux.

V0.7.8

• Update TaiChi version to 1.4.0.

• Set SciencePlots as a optional package.

• Fix bug in create_polycrystalline.