Added readme (#4)

Co-authored-by: yanghan-microsoft <hanyang@microsoft.com>

README.md

@@ -1 +1,62 @@
-# MatterSim
+<h1 align="center">MatterSim</h1>
+
+<h4 align="center">
+
+[![arXiv](https://img.shields.io/badge/arXiv-2405.04967-blue?logo=arxiv&logoColor=white.svg)](https://arxiv.org/abs/2405.04967)
+[![Requires Python 3.9+](https://img.shields.io/badge/Python-3.9+-blue.svg?logo=python&logoColor=white)](https://python.org/downloads)
+
+</h4>
+
+
+MatterSim is a deep learning atomistic model across elements, temperatures and pressures.
+
+## Installation
+### Install from PyPI
+TODO: To be released to PyPI.
+
+
+### Install from source code
+Requirements:
+- Python == 3.9
+
+To install the package, run the following command under the root of the folder:
+```bash
+conda env create -f environment.yaml
+conda activate mattersim
+pip install -e .
+```
+
+### Obtain the model checkpoints
+You may obtain our model checkpoints from
+```bash
+# link to somewhere
+```
+
+## Usage
+### A minimal test
+```python
+from mattersim.forcefield.potential import Potential
+from mattersim.datasets.utils.build import build_dataloader
+
+potential = Potential.load(load_path="/path/to/checkpoint", device="cuda:0")
+from ase.build import bulk
+si = bulk("Si", "diamond", a=5.43)
+dataloader = build_dataloader([si], only_inference=True, model_type=model_name)
+predictions = potential.predict_properties(dataloader, include_forces=True, include_stresses=True)
+print(predictions)
+```
+
+
+## Reference
+If you use MatterSim, please cite our preprint on [arXiv](https://arxiv.org/abs/2405.04967):
+```
+@article{yang2024mattersim,
+  title={Mattersim: A deep learning atomistic model across elements, temperatures and pressures},
+  author={Yang, Han and Hu, Chenxi and Zhou, Yichi and Liu, Xixian and Shi, Yu and Li, Jielan and Li, Guanzhi and Chen, Zekun and Chen, Shuizhou and Zeni, Claudio and others},
+  journal={arXiv preprint arXiv:2405.04967},
+  year={2024}
+}
+```
+
+## Researcher and Developers
+MatterSim is currently in active development. If you have any specific research interests related to this model or encounter any issues, please don't hesitate to reach out to us.