Reaction-network is a Python package for predicting likely inorganic chemical reaction pathways using graph theory.
Reason this release was yanked:
improper release
Project description
Reaction network (rxn-network) is a Python package for predicting chemical reaction pathways in solid-state materials synthesis using combinatorial and graph-theorteical methods.
Installation directions
The rxn-network package has several dependencies, most of which can be installed through PyPI. However, graph-tool must be installed through a more customized method; please see https://graph-tool.skewed.de/ for more details. We recommend the following installation procedure which creates a new conda environment with Python 3.9, activates it, and then installs graph-tool through conda-forge.
conda create -n gt python=3.9
conda activate gt
conda install -c conda-forge graph-tool
Reaction network can then simply be installed via pip:
pip install reaction-network
For developers:
To install an editable version of the rxn-network code, simply clone the code from this repository, navigate to its directory, and then run the following command to install the requirements:
pip install -r requirements.txt
pip install -e .
Note that this only works if the repository is cloned from GitHub, such that it contains the proper metadata.
Tutorial notebooks
The notebooks folder contains two (2) demonstration notebooks:
- enumerators.ipynb: how to enumerate reactions from a set of entries; running enumerators using Fireworks
- network.ipynb: how to build reaction networks from a list of enumerators and entries; how to perform pathfinding to recommend balanced reaction pathways; running reaction network analysis using Fireworks
Citation
If you use this code or Python package in your work, please consider citing the following paper:
McDermott, M. J., Dwaraknath, S. S., and Persson, K. A. (2021). A graph-based network for predicting chemical reaction pathways in solid-state materials synthesis. Nature Communications, 12(1). https://doi.org/10.1038/s41467-021-23339-x
Acknowledgements
This work was supported as part of GENESIS: A Next Generation Synthesis Center, an Energy Frontier Research Center funded by the U.S. Department of Energy, Office of Science, Basic Energy Sciences under Award Number DE-SC0019212.
Learn more about the GENESIS EFRC here: https://www.stonybrook.edu/genesis/
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