Update README.md

README.md

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 This repository contains the dataset used in the paper: **"Scalable Machine Learning Force Fields for Macromolecular Systems Through Long-Range Aware Message Passing"** [[link](https://www.google.com/search?q=URL_ADDRESS)].
 
+### Dataset Components
+The dataset consists of three primary compressed archives, each catering to different aspects of macromolecular force field modeling:
+
+1. deshaw_protein.tar.gz
+
+This subset contains protein structures and conformational data extracted from the DE Shaw Research molecular dynamics trajectories [1]. It provides high-fidelity biological samples essential for evaluating the model's ability to generalize across complex protein folding and fluctuation landscapes.
+
+2. di_molecule_interaction.tar.gz
+
+This dataset focuses on non-bonded interactions. It was constructed by systematically increasing the distance between two distinct molecules (dimers). It is specifically designed to benchmark long-range interaction modeling, capturing how energy and forces decay as a function of intermolecular separation.
+
+3. md_traj.tar.gz
+
+This file contains various molecular dynamics (MD) trajectories generated in-house. It includes a diverse set of chemical systems (e.g., NaCl in water) used to train and validate the model's stability and accuracy in simulating temporal evolution and thermodynamic properties.
+
+### How-to Load
 The data is stored in **LMDB** format. Using the `md_traj` data as an example, you can load the data as follows:
 
 ```python
@@ -50,3 +66,7 @@ For the **Di-Molecule** dataset, the following additional properties are include
 * `distance`: The horizontal distance between the right-most atom of A and the left-most atom of B, in units of Angstrom.
 
 For further details, please refer to the [original paper](https://www.google.com/search?q=URL_ADDRESS) and the official repository: [IQuestLab/E2Former](https://github.com/IQuestLab/E2Former).
+
+More details about the data please refer to the original paper [[link](https://www.google.com/search?q=URL_ADDRESS)].
+
+[1]: Lindorff-Larsen, et al. "How fast-folding proteins fold." Science 334.6055 (2011): 517-520.