ACEHAL

KO | EN

gitlite — search

#python #java #python3 #arduino #golang #machine-learning #rust #html #flask #javascript #seismology #nodejs

★ 16 Open GitHub ↗

No description available.

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

## ACEHAL: Hyperactive Learning (HAL) Python interface for building Atomic Cluster Expansion potentials (ACE1.jl/ACE1x.jl)

This package builds ACE interatomic potentials using Hyperactive Learning (HAL). Written by Cas van der Oord and Noam Bernstein.

### HAL installation:

1. install Julia 1.8.5 and python 3.9.x (with python ase, scikit-learn, matplotlib and numpy installed)
2. run Julia command

```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`
   
3. install `julia` Python package to set up Python -> Julia connection

```python -m pip install julia==0.6.1```

```python -c "import julia; julia.install()"```

4. Install this package by ```pip install . ``` or ```python setup.py install``` after cloning this repo

### ACE1 potentials in Python:

After 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

Example scripts can be found in the scripts folder.

### References:

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},
}
```

ACEHAL

Explore Similar Repositories

Was this content helpful?

Recent Feedback

Download README