Update README.md

README.md

@@ -1,3 +1,49 @@
----
-license: mit
----
+---
+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)
+```