Update README.md

README.md

@@ -4,15 +4,41 @@ tags:
 - Machine Learning Interatomic Potential
 ---
 
-# Model Card for mace-unversal
+# Model Card for mace-universal
 
 [MACE](https://github.com/ACEsuit/mace) (Multiple Atomic Cluster Expansion) is a machine learning interatomic potential (MLIP) with higher order equivariant message passing. For more information about MACE formalism, please see authors' [paper](https://arxiv.org/abs/2206.07697). 
 
 
 [2023-08-14-mace-universal.model](https://huggingface.co/cyrusyc/mace-universal/blob/main/2023-08-14-mace-universal.model) was trained with MPTrj data, [Materials Project](https://materialsproject.org) relaxation trajectories compiled by [CHGNet](https://arxiv.org/abs/2302.14231) authors to cover 89 elements and 1.6M configurations. The checkpoint was used for materials stability prediction in [Matbench Discovery](https://matbench-discovery.materialsproject.org/) and the corresponding [preprint](https://arXiv.org/abs/2308.14920).
 
+# Usage
 
-# Citation
+1. (optional) Install Pytorch, [ASE](https://wiki.fysik.dtu.dk/ase/) prerequisites for specific version
+2. Install [MACE](https://github.com/ACEsuit/mace) through GitHub (not through pypi)
+
+```shell
+pip install git+https://github.com/ACEsuit/mace.git
+```
+3. Use MACECalculator
+
+```python
+from mace.calculators import MACECalculator
+from ase.md.npt import NPT
+
+calculator = MACECalculator(
+  model_paths=/path/to/pretrained.model,
+  device=device
+)
+
+nvt = NPT(
+  atoms=atoms,
+  timestep=timestep,
+  temperature_K=temperature,
+  externalstress=externalstress,
+)
+```
+
+# Citing
 
 If you use the pretrained models in this repository, please cite all the following: