Introduction

SCINE Parrot is a wrapper for machine learning potentials. It is an interface to

• lifelong machine learning potentials (lMLP)

• ANI (TorchANI)

• M3GNet (MatGL)

• MACE (MACE)

Each method is represented by its own Calculator and the entire wrapper constitutes a SCINE module that can be loaded dynamically at runtime. For more information on these concepts see the Scine::Core repository.

The full list of models includes

• Model(“lmlp”, “<generalization_setting_file>”, “”)

• Model(“ani”, “ani2x”, “”)

• Model(“ani”, “ani1ccx”, “”)

• Model(“ani”, “ani1x”, “”)

• Model(“m3gnet”, “m3gnet-mp-2021.2.8-pes”, “”)

• Model(“m3gnet”, “m3gnet-mp-2021.2.8-direct-pes”, “”)

• Model(“mace”, “mace-mp_large”, “”)

• Model(“mace”, “mace-mp_medium”, “”)

• Model(“mace”, “mace-mp_small”, “”)

• Model(“mace”, “mace-off_large”, “”)

• Model(“mace”, “mace-off_medium”, “”)

• Model(“mace”, “mace-off_small”, “”)

License and Copyright Information

Parrot is distributed under the BSD 3-clause “New” or “Revised” License. For more license and copyright information, see the file LICENSE.txt in the repository.

git clone <parrot-repo>
pip install ./parrot

cd parrot
make -C docs html

<browser name> docs/build/html/index.html

cd parrot
pip install -r requirements-dev.txt

How to Cite

When publishing results obtained with SCINE Parrot, please cite the corresponding release as archived on Zenodo (please use the DOI of the respective release). In addition, when publishing results obtained with other SCINE modules we kindly ask you to cite the appropriate references.

This wrapper should also not be mistaken for the respective actual machine learning potential code it wraps. For the latter code and its citations, we refer you to the respective publication/webpage.

Support and Contact

In case you should encounter problems or bugs, please write a short message to scine@phys.chem.ethz.ch.