AutoCat is a suite of python tools for sequential learning for materials applications and automating structure generation for DFT catalysis studies. Documentation for the package can be found here.
Development of this package stems from ACED, as part of the ARPA-E DIFFERENTIATE program.
There are two options for installation, either via pip or from the repo directly.
If you are planning on strictly using AutoCat rather than contributing to development,
we recommend using pip within a virtual environment (e.g.
conda
). This can be done as follows:
pip install autocat
Alternatively, if you would like to contribute to the development of this software,
AutoCat can be installed via a clone from Github. First, you'll need to clone the
github repo to your local machine (or wherever you'd like to use AutoCat) using
git clone. Once the repo has been cloned, you can install AutoCat as an editable
package by changing into the created directory (the one with setup.py) and installing
via:
pip install -e .
Contributions through issues, feature requests, and pull requests are welcome. Guidelines are provided here.
The code presented herein was funded by the Advanced Research Projects Agency-Energy (ARPA-E), U.S. Department of Energy, under Award Number DE-AR0001211 and in part by the National Science Foundation, under Award Number CBET-1554273. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof.