IPS provides you with tools to generate Machine Learned Interatomic Potentials. You can find the documentation at https://ipsuite.readthedocs.io
Install the package to get started or check out an interactive notebook
pip install ipsuite
IPSuite relies on third-party ML packages. As these often come with different,
sometimes incompatible requirements, an ipsuite
installation is barebones. If
you want to run your favorite ML code but are encountered with an import error,
please install the package manually. You can look at the pyproject.toml
to
find the packages ipsuite
is tested against.
Examples can be found at:
- https://dagshub.com/PythonFZ/IPS-Examples/src/intro/main.ipynb
- https://dagshub.com/PythonFZ/IPS-Examples/src/graph/main.ipynb
- https://dagshub.com/PythonFZ/IPS-Examples/src/modify_graph/main.ipynb
We provide an IPSuite docker image for Linux that includes the apax
, mace
and gap
MLPs. You can use IPSuite directly from within the image by calling:
docker run -it -v "$(pwd):/app" --gpus all pythonf/ipsuite dvc repro
docker run -it -v "$(pwd):/app" --gpus all pythonf/ipsuite python
docker run -it -v "$(pwd):/app" --gpus all pythonf/ipsuite zntrack list
docker run -it -v "$(pwd):/app" --gpus all --rm -p 8888:8888 pythonf/ipsuite jupyter lab --ip=0.0.0.0 --port=8888 --allow-root
Running dvc repro
via the docker container will create files owned by
root:root
. If you solely use docker this will not cause any issues. If you
switch between docker and a dvc
version on your host system, you might
encounter permission errors. You can resolve them, by changing the ownership of
the files. You can do this via the host chown "$(id -u):$(id -g)" -R .
or from
inside the docker container:
echo $(id -u):$(id -g)
docker run -it -v "$(pwd):/app" pythonf/ipsuite /bin/bash
addgroup --gid $GROUP_ID user
adduser --disabled-password --gecos '' --uid $USER_ID --gid $GROUP_ID user
chown user:user -R .
If you use IPSuite in your research and find it helpful please consider citing us.
@article{zillsCollaborationMachineLearnedPotentials2024,
title = {Collaboration on {{Machine-Learned Potentials}} with {{IPSuite}}: {{A Modular Framework}} for {{Learning-on-the-Fly}}},
shorttitle = {Collaboration on {{Machine-Learned Potentials}} with {{IPSuite}}},
author = {Zills, Fabian and Schäfer, Moritz René and Segreto, Nico and Kästner, Johannes and Holm, Christian and Tovey, Samuel},
date = {2024-04-03},
journaltitle = {The Journal of Physical Chemistry B},
shortjournal = {J. Phys. Chem. B},
publisher = {American Chemical Society},
issn = {1520-6106},
doi = {10.1021/acs.jpcb.3c07187},
}
@misc{zillsZnTrackDataCode2024,
title = {{{ZnTrack}} -- {{Data}} as {{Code}}},
author = {Zills, Fabian and Sch{\"a}fer, Moritz and Tovey, Samuel and K{\"a}stner, Johannes and Holm, Christian},
year = {2024},
eprint={2401.10603},
archivePrefix={arXiv},
}