Mdgo documentation

Introduction

Welcome to the documentation site for mdgo! Mdgo is an python toolkit for classical molecualr dynamics (MD) simulation setup and results analysis, especially for electrolyte systems. The purpose of making this package is for supporting a high-throughput workflow for screening novel electrolytes for battery use. Currently, the package is under active development.

Features

Please see modules for detailed documentation of the code.

  1. Retriving compound structure and information from PubChem

    • Supported searching text:

      • cid, name, smiles, inchi, inchikey or formula

    • Supported output format:

      • smiles code, PDB, XML, ASNT/B, JSON, SDF, CSV, PNG, TXT

  2. Retrieving water and ion models

    • Supported water models:

      • SCP, SPC/E, TIP3P_EW, TIP4P_EW, TIP4P_2005

    • Supported ion models:

      • alkali, ammonium, and halide monovalent ions by Jensen and Jorgensen

      • alkali and halide monovalent ions by Joung and Cheatham

      • alkali and alkaline-earth metal cations by Åqvist

  3. Write OPLS-AA forcefield file from LigParGen

    • Supported input format:

      • mol/pdb

      • SMILES code

    • Supported output format:

      • LAMMPS(.lmp)

      • GROMACS(.gro, .itp)

  4. Write OPLS-AA forcefield file from Maestro

  5. Packmol wrapper

    • Supported input format:

      • xyz

      • Others pending…

  6. Basic simulation properties

    • Initial box dimension

    • Equilibrium box dimension

    • Salt concentration

  7. Conductivity analysis

    • Green–Kubo conductivity

    • Nernst–Einstein conductivity

  8. Coordination analysis

    • The distribution of the coordination number of single species

    • The integral of radial distribution function (The average coordination numbers of multiple species)

    • Solvation structure write out

    • Population of solvent separated ion pairs (SSIP), contact ion pairs (CIP), and aggregates (AGG)

    • The trajectory (distance) of cation and coordinating species as a function of time

    • The hopping frequency of cation between binding sites

    • The distribution heat map of cation around binding sites

    • The averaged nearest neighbor distance of a species

  9. Diffusion analysis

    • The mean square displacement of all species

    • The mean square displacement of coordinated species and uncoordinated species, separately

    • Self-diffusion coefficients

  10. Residence time analysis

    • The residence time of all species

Installation

Installing from PyPI

To install the latest release version of mdgo:

pip install mdgo

Installing from Source

Mdgo requires numpy, pandas, matplotlib, scipy, tqdm, statsmodels, pymatgen>=2022.0.9, pubchempy, selenium, MDAnalysis (version 2.0.0-dev0 prefered) and their dependencies.

Getting source code

If not available already, use the following steps.

  1. Install git if not already packaged with your system.

  2. Download the mdgo source code using the command:

    git clone https://github.com/htz1992213/mdgo.git

Installation from source

  1. Navigate to mdgo root directory:

    cd mdgo

  2. Install the code, using the command:

    pip install .

  3. The latest version MDAnalysis==2.0.0.dev0 is recommended, you may download the source code of the latest MDAnalysis from github and install using pip to replace an existing version.

Installation in development mode

  1. Navigate to mdgo root directory:

    cd mdgo

  2. Install the code in “editable” mode, using the command:

    pip install -e .

  3. The latest version MDAnalysis==2.0.0.dev0 is recommended, you may download the source code of the latest MDAnalysis from github and install using pip to replace an existing version.

Contributing

Reporting bugs

Please report any bugs and issues at mdgo’s Github Issues page.

Developing new functionality

You may submit new code/bugfixes by sending a pull request to the mdgo’s github repository.

How to cite mdgo

pending…

License

Mdgo is released under the MIT License. The terms of the license are as follows:

MIT License

Copyright (c) 2020-2021 MDGO development team

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.

About the Team

Tingzheng Hou started mdgo in 2020 under the supervision of Prof. Kristin Persson at University of California, berkeley.

Indices and tables