Update README.md

README.md

@@ -23,26 +23,3 @@ We present `pos-egnn`, a Position-based Equivariant Graph Neural Network foundat
 Besides the model weigths `pos-egnn.v1-6M.pt`, which can be downloaded from [HuggingFace](https://huggingface.co/ibm-research/materials.pos-egnn), we also provide examples for performing inference, feature extraction and molecular dynamics simulation with the model (`example.ipynb`).
 
 For more information, please reach out to rneumann@br.ibm.com and/or flaviu.cipcigan@ibm.com
-
-## Table of Contents
-1. [**Getting Started**](#getting-started)
-2. [**Example**](#example)
-
-## Getting Started
-Follow these steps to replicate our environment and install the necessary libraries:
-
-First, make sure to have Python 3.11 installed. Then, to create the virtual environment, run the following commands:
-
-```bash
-python3.11 -m venv env
-source env/bin/activate
-```
-
-Run  the following command to install the library dependencies.
-
-```bash
-pip install -r requirements.txt
-```
-
-## Example
-Please refer to the `example.ipynb` for a step-by-step demonstration on how to perform inference, feature extraction and molecular dynamics simulation with the model.