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license: mit
---
# EScAIP: Efficiently Scaled Attention Interatomic Potential
## Installation
First, clone the FAIR Chem repo with allscaip branch:
```bash
git clone -b allscaip https://github.com/EricZQu/fairchem.git
cd fairchem
```
Then, create a conda environment and install the dependencies:
```bash
conda create -n allscaip python=3.12
conda activate allscaip
pip install -e packages/fairchem-core[dev]
```
## Inference
You can use the `FAIRChemCalculator` to load a pretrained EScAIP model and perform inference. Here's an example:
```python
from ase import units
from ase.io import Trajectory
from ase.md.langevin import Langevin
from ase.build import molecule
from fairchem.core import pretrained_mlip, FAIRChemCalculator
calc = FAIRChemCalculator.from_model_checkpoint("/path/to/your/checkpoint.pt", task_name="omol")
atoms = molecule("H2O")
atoms.calc = calc
dyn = Langevin(
atoms,
timestep=0.1 * units.fs,
temperature_K=400,
friction=0.001 / units.fs,
)
trajectory = Trajectory("my_md.traj", "w", atoms)
dyn.attach(trajectory.write, interval=1)
dyn.run(steps=1000)
```
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