xdatbus 0.2.5

A Python package for post-analysis of VASP AIMD data

xdatbus

Xdatbus is a Python package designed specifically for Vienna Ab-initio Simulation Package (VASP) users conducting ab-initio molecular dynamics (AIMD) simulations, as well as biased MD simulations. The name of the package is derived from XDATCAR, which represents the combined AIMD trajectories generated by VASP. Documentation for the package can be accessed here and the Jupyter Notebook tutorial is also available.

Installation

Make sure you have a Python interpreter, preferably version 3.10 or higher. Then, you can simply install xdatbus from PyPI using pip:

pip install xdatbus

If you'd like to use the latest unreleased version on the main branch, you can install it directly from GitHub:

pip install -U git+https://github.com/jcwang587/xdatbus

The package is also availabe from conda-based installation. It is generally recommended you first create a separate environment, then you can install via the xdatbus channel on Anaconda cloud:

conda install --channel xdatbus xdatbus

If you plan to use PLUMED to analyze biased MD sampling results, you can also install the conda version of PLUMED together:

conda install -c xdatbus -c conda-forge xdatbus plumed

Get Started

This is a brief example demonstrating how to use the basic function of xdatbus to aggregate multiple xdatcar files into a single file and unwrap the coordinates into an .xyz file:

import os
from xdatbus import xdc_aggregate, xdc_unwrap

xdc_dir = "./xdatcar_dir"
xdb_dir = os.path.dirname(xdc_dir)
xdb_path = os.path.join(xdb_dir, "XDATBUS")
xyz_path = os.path.join(xdb_dir, "XDATBUS_unwrap.xyz")

xdc_aggregate(xdc_dir=xdc_dir, output_dir=xdb_dir)
xdc_unwrap(xdc_path=xdb_path, output_path=xyz_path)

There are also entry points included with the installation for the Command Line Interface (CLI) to perform similar tasks (do not include the $ when copying):

$ xdc_aggregate --xdc_dir ./xdatcar --output_dir ./

$ xdc_unwrap --xdc_path ./XDATBUS --output_path ./XDATBUS_unwrap.xyz

Major Changelog

0.2.2 Enabled CLI with enhanced interaction through the rich package.

0.2.0 Implemented the preparation of training data for MACE machine learning interatomic potentials.