This package builds ACE interatomic potentials using Hyperactive Learning (HAL). Written by Cas van der Oord and Noam Bernstein.
using Pkg; pkg"registry add https://github.com/JuliaRegistries/General"; pkg"registry add https://github.com/ACEsuit/ACEregistry"; pkg"add ACE1, ACE1x, ASE, JuLIP"
make sure you have at least ACE1 version = 0.11.4 and ACE1x = 0.0.4. Use Pkg.activate(".")
to use a local project and set environment variable JULIA_PROJECT
accordingly. A working Project.toml
can be found in /tests/julia_assets/Project.toml
julia
Python package to set up Python -> Julia connection python -m pip install julia==0.6.1
python -c "import julia; julia.install()"
pip install .
or python setup.py install
after cloning this repoAfter installation of julia
Python package (see 3. above) ACE1x potentials (.json) can be used by first installing pyjulip
.
git clone https://github.com/casv2/pyjulip.git
cd pyjulip
pip install .
Python ASE calculators are set up using pyjulip.ACE1("filename.json")
Example scripts can be found in the scripts folder.
If using this code please reference
@misc{van2022hyperactive,
doi = {10.48550/ARXIV.2210.04225},
url = {https://arxiv.org/abs/2210.04225},
author = {van der Oord, Cas and Sachs, Matthias and Kov{\'a}cs, D{\'a}vid P{\'e}ter and Ortner, Christoph and Cs{\'a}nyi, G{\'a}bor},
title = {Hyperactive Learning (HAL) for Data-Driven Interatomic Potentials},
publisher = {arXiv},
year = {2022},
}
@article{DUSSON2022110946,
title = {Atomic cluster expansion: Completeness, efficiency and stability},
journal = {Journal of Computational Physics},
volume = {454},
pages = {110946},
year = {2022},
issn = {0021-9991},
doi = {https://doi.org/10.1016/j.jcp.2022.110946},
url = {https://www.sciencedirect.com/science/article/pii/S0021999122000080},
}