diff --git a/README.md b/README.md
index fe25e63..0eb37f8 100644
--- a/README.md
+++ b/README.md
@@ -5,12 +5,47 @@ properties of crystals such as the elasticity tensor.
 
 MatTen is an equivariant graph neural network built using [e3nn](https://github.com/e3nn/e3nn).
 
-
 ## Install
 
-## Use pretrained model
+Follow the official documentation to install the dependencies: [pytorch>=2.0.0](https://pytorch.org/get-started/locally/),
+[pyg>=2.3.0](https://pytorch-geometric.readthedocs.io/en/latest/notes/installation.html),
+and [pytorch-lightning>=2.0.0](https://lightning.ai/docs/pytorch/latest/).
+Then
+
+```
+git clone https://github.com/wengroup/matten.git
+pip install -e matten
+```
+
+## Use the pretrained model
 
 [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/wengroup/matten/blob/main/notebooks/predict_colab.ipynb)
 
+```python
+from pymatgen.core import Structure
+from matten.predict import predict
+
+
+def get_structure():
+    a = 5.46
+    lattice = [[0, a / 2, a / 2], [a / 2, 0, a / 2], [a / 2, a / 2, 0]]
+    basis = [[0.0, 0.0, 0.0], [0.25, 0.25, 0.25]]
+    Si = Structure(lattice, ["Si", "Si"], basis)
+
+    return Si
+
+
+structure = get_structure()
+
+elasticity_tensor = predict(structure)
+```
+
+The `predict` function returns an elasticity tensor. To make predictions for multiple
+crystals, pass a list of structures to `predict`.
 
 ## Data
+
+- An example of 100 crystals is available in the [datasets](./datasets) directory.
+- The full dataset is available at: [TODO]
+
+## Reference