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https://github.com/FAIR-Chem/fairchem
FAIR Chemistry's library of machine learning methods for chemistry
https://github.com/FAIR-Chem/fairchem
Last synced: 2 months ago
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FAIR Chemistry's library of machine learning methods for chemistry
- Host: GitHub
- URL: https://github.com/FAIR-Chem/fairchem
- Owner: FAIR-Chem
- License: other
- Created: 2019-09-26T04:47:27.000Z (over 5 years ago)
- Default Branch: main
- Last Pushed: 2024-11-13T00:25:29.000Z (2 months ago)
- Last Synced: 2024-11-13T01:22:01.356Z (2 months ago)
- Language: Python
- Homepage: https://opencatalystproject.org/
- Size: 330 MB
- Stars: 874
- Watchers: 28
- Forks: 258
- Open Issues: 29
-
Metadata Files:
- Readme: README.md
- License: LICENSE.md
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README
fairchem by FAIR Chemistry
[](https://github.com/codespaces/new/FAIR-Chem/fairchem?quickstart=1)
`fairchem` is the [FAIR](https://ai.meta.com/research/) Chemistry's centralized repository of all its data, models, demos, and application efforts for materials science and quantum chemistry.
### Documentation
If you are looking for `Open-Catalyst-Project/ocp`, it can now be found at [`fairchem.core`](src/fairchem/core). Visit its corresponding documentation [here](https://fair-chem.github.io/).
### Contents
The repository is organized into several directories to help you find what you are looking for:
- [`fairchem.core`](src/fairchem/core): State of the art machine learning models for materials science and chemistry
- [`fairchem.data`](src/fairchem/data): Dataset downloads and input generation codes
- [`fairchem.demo`](src/fairchem/demo): Python API for the [Open Catalyst Demo](https://open-catalyst.metademolab.com/)
- [`fairchem.applications`](src/fairchem/applications): Follow up applications and works (AdsorbML, CatTSunami, etc.)
### Installation
Packages can be installed in your environment by the following:
```
pip install -e packages/fairchem-{fairchem-package-name}
```
`fairchem.core` requires you to first create your environment
- [Installation Guide](https://fair-chem.github.io/core/install.html)
### Quick Start
Pretrained models can be used directly with ASE through our `OCPCalculator` interface:
```python
from ase.build import fcc100, add_adsorbate, molecule
from ase.optimize import LBFGS
from fairchem.core import OCPCalculator
# Set up your system as an ASE atoms object
slab = fcc100('Cu', (3, 3, 3), vacuum=8)
adsorbate = molecule("CO")
add_adsorbate(slab, adsorbate, 2.0, 'bridge')
calc = OCPCalculator(
model_name="EquiformerV2-31M-S2EF-OC20-All+MD",
local_cache="pretrained_models",
cpu=False,
)
slab.calc = calc
# Set up LBFGS dynamics object
dyn = LBFGS(slab)
dyn.run(0.05, 100)
```
If you are interested in training your own models or fine-tuning on your datasets, visit the [documentation](https://fair-chem.github.io/) for more details and examples.
### Why a single repository?
Since many of our repositories rely heavily on our other repositories, a single repository makes it really easy to test and ensure consistency across repositories. This should also help simplify the installation process for users who are interested in integrating many of the efforts into one place.
### LICENSE
`fairchem` is available under a [MIT License](LICENSE.md).