https://github.com/ACEsuit/ACEHAL

https://github.com/ACEsuit/ACEHAL

Last synced: about 2 months ago
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• Host: GitHub
• URL: https://github.com/ACEsuit/ACEHAL
• Owner: ACEsuit
• Created: 2023-02-24T17:33:47.000Z (almost 2 years ago)
• Default Branch: main
• Last Pushed: 2023-10-01T12:19:41.000Z (over 1 year ago)
• Last Synced: 2024-08-02T19:01:38.128Z (5 months ago)
• Language: Python
• Size: 168 KB
• Stars: 11
• Watchers: 5
• Forks: 7
• Open Issues: 4
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

• best-of-atomistic-machine-learning - GitHub - 40% open · ⏱️ 21.09.2023): (Active learning)

README

        
## 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},

}

```