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.
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
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
Write OPLS-AA forcefield file from LigParGen
Supported input format:
mol/pdb
SMILES code
Supported output format:
LAMMPS(.lmp)
GROMACS(.gro, .itp)
Write OPLS-AA forcefield file from Maestro
Supported input format:
Supported output format:
LAMMPS(.lmp)
Others pending…
Packmol wrapper
Supported input format:
xyz
Others pending…
Basic simulation properties
Initial box dimension
Equilibrium box dimension
Salt concentration
Conductivity analysis
Green–Kubo conductivity
Nernst–Einstein conductivity
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
Diffusion analysis
The mean square displacement of all species
The mean square displacement of coordinated species and uncoordinated species, separately
Self-diffusion coefficients
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.
Install git if not already packaged with your system.
Download the mdgo source code using the command:
git clone https://github.com/htz1992213/mdgo.git
Installation from source
Navigate to mdgo root directory:
cd mdgo
Install the code, using the command:
pip install .
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
Navigate to mdgo root directory:
cd mdgo
Install the code in “editable” mode, using the command:
pip install -e .
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.
Copyright Policy
The following banner should be used in any source code file to indicate the copyright and license terms:
# Copyright (c) Tingzheng Hou.
# Distributed under the terms of the MIT License.