Description:
qmflows 1.0.0
QMFlows
See documentation for tutorials and documentation.
Motivation
Research on modern computational quantum chemistry relies on a set of computational
tools to carry out calculations. The complexity of the calculations usually requires
intercommunication between the aforementioned tools, such communication is usually done
through shell scripts that try to automate input/output actions like: launching
the computations in a cluster, reading the resulting output and feeding the relevant
numerical result to another program. Such scripts are difficult to maintain and extend,
requiring a significant programming expertise to work with them. Being then desirable a
set of automatic and extensible tools that allows to perform complex simulations in
heterogeneous hardware platforms.
This library tackles the construction and efficient execution of computational chemistry workflows.
This allows computational chemists to use the emerging massively parallel compute environments in
an easy manner and focus on interpretation of scientific data rather than on tedious job submission
procedures and manual data processing.
Description
This library consists of a set of modules written in Python3 to
automate the following tasks:
Input generation.
Handle tasks dependencies (Noodles).
Advanced molecular manipulation capabilities with (rdkit).
Jobs failure detection and recovery.
Numerical data storage (h5py).
Tutorial and Examples
A tutorial written as a jupyter-notebook is available from: tutorial-qmflows. You can
also access direclty more advanced examples.
Installation
Download miniconda for python3: miniconda (also you can install the complete anaconda version).
Install according to: installConda.
Create a new virtual environment using the following commands:
conda create -n qmflows
Activate the new virtual environment
source activate qmflows
To exit the virtual environment type source deactivate.
Dependencies installation
Type in your terminal:
conda activate qmflows
Using the conda environment the following packages should be installed:
install rdkit and h5py using conda:
conda install -y -q -c conda-forge rdkit h5py
Note that rdkit is optional for Python 3.7 and later.
Package installation
Finally install the package:
Install QMFlows using pip:
- pip install qmflows
Now you are ready to use qmflows.
Notes:
Once the libraries and the virtual environment are installed, you only need to type
conda activate qmflows each time that you want to use the software.
License
For personal and professional use. You cannot resell or redistribute these repositories in their original state.
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