GithubHelp home page GithubHelp logo

aimd's Introduction

aimd

This is designed to help researchers to perform diffusion analysis from ab inito molecular dynamic (AIMD) simulations. It contains some useful python classes which can be integrated into your python analysis code, and one easy-to-use executable python script which can perform diffusion analysis on the terminal without further modification.

The executable python script (script/analyze_aimd.py) has two main functions:

  1. Analyze diffusion properties from AIMD calculations at one single temperature.

  2. Analyze diffusion properties from AIMD calculations at multiple temperatures.

    More information can be found by the behind code after you install the library

    analyze_aimd -h
    analyze_aimd diffusivity -h
    analyze_aimd arrhenius -h

Citing

If you use this library, please cite the following papers:

He, Xingfeng, Yizhou Zhu, Alexander Epstein, and Yifei Mo. "Statistical variances 
of diffusional properties from ab initio molecular dynamics simulations." 
npj Computational Materials 4, no. 1 (2018): 18.

http://dx.doi.org/10.1038/s41524-018-0074-y

He, Xingfeng, Yizhou Zhu, and Yifei Mo. "Origin of fast ion diffusion in super-ionic 
conductors." Nature communications 8 (2017): 15893.

http://dx.doi.org/10.1038/ncomms15893

Mo, Yifei, Shyue Ping Ong, and Gerbrand Ceder. "First principles study of the Li10GeP2S12 
lithium super ionic conductor material." Chemistry of Materials 24, no. 1 (2011): 15-17.

http://dx.doi.org/10.1021/cm203303y

Ong, Shyue Ping, William Davidson Richards, Anubhav Jain, Geoffroy Hautier, Michael Kocher, 
Shreyas Cholia, Dan Gunter, Vincent L. Chevrier, Kristin A. Persson, and Gerbrand Ceder. 
"Python Materials Genomics (pymatgen): A robust, open-source python library for materials 
analysis." Computational Materials Science 68 (2013): 314-319. 

https://doi.org/10.1016/j.commatsci.2012.10.028

Install and test steps

  1. Install all dependency and this library

    python setup.py install

    if you have no root access, you may need to use

    python setup.py install --user
  2. Try to import python classes in your python console

    from aimd.diffusion import DiffusivityAnalyzer, ErrorAnalysisFromDiffusivityAnalyzer, \
    ArreheniusAnalyzer
  3. The setup.py will automatically create an executable file analyze_aimd into your PATH. Try to call it from terminal and read the documentations:

    analyze_aimd -h
    analyze_aimd diffusivity -h
    analyze_aimd arrhenius -h
  4. Use the provided test files to perform diffusion analysis.

    a. go to the folder aimd/tests/tests_files/latp_md

    b. run in terminal

    analyze_aimd diffusivity Li+ RUN_ 10 29 3.2

    c. You can ignore the warning msgs. You will get the diffusion results. The diffusivity is ~7.6e-5 cm^2/s, conductivity is ~583 mS/cm

    d. go to folder aimd/tests/tests_files/arrhenius

    e. run in terminal

    analyze_aimd arrhenius D_T.csv -p POSCAR -T 300 -s Li+
    analyze_aimd arrhenius D_T.csv -p POSCAR -T 300 -s Li+ --plot

    f. You will get the arrhenius relationship of LATP. The conductivity at 300K is predicted to be ~1.08 mS/cm, Ea is ~0.258 eV +- 0.017 eV. If your x11 windows settings are correct or you are at local computer, you will have a plot window pop up to show the arrhenius relationship for cmd with -plot option.

Standalone executable binary

We also generated standalong executable binary file for script/analyze_aimd.py, which can be found at the release page: https://github.com/mogroupumd/aimd/releases . The binary file included Python interpreter and all dependencies required by the analyze_aimd.py. Thus the user can run the packaged script without installing a Python interpreter or any other modules. The user documentations is same as in the https://github.com/mogroupumd/aimd#install-and-testing-steps

License

Python library aimd is released under the MIT License. The terms of the license are as follows:

The MIT License (MIT) Copyright (c) 2018 UMD 
 
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated 
documentation files (the "Software"), to deal in the Software without restriction, including without limitation 
the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and 
to permit persons to whom the Software is furnished to do so, subject to the following conditions:
 
The above copyright notice and this permission notice shall be included in all copies or substantial portions of 
the Software.
 
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO 
THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 
SOFTWARE.

aimd's People

Contributors

xingfenghe avatar yifeimo avatar

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    ๐Ÿ–– Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. ๐Ÿ“Š๐Ÿ“ˆ๐ŸŽ‰

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google โค๏ธ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.