Update README.md

README.md

@@ -2,40 +2,51 @@
 license: mit
 tags:
 - chemistry
+- biology
 ---
-Here is the dataset used in paper `Scalable Machine Learning Force Fields for Macromolecular Systems Through Long-Range Aware Message Passing` [[link](URL地址)].
 
-The data is stored in lmdb formate. Taking `md_traj` data as an example, you can load the data as following:
+
+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)].
+
+The data is stored in **LMDB** format. Using the `md_traj` data as an example, you can load the data as follows:
+
 ```python
 import lmdb
 import pickle
-env = lmdb.open("md_traj/train/NaCl/data_0.lmdb", subdir=False)
-txn = env.begin()
-length = txn.stat()['entries']
-data_list = []
-for idx in range(length):
-    data.append(pickle.loads(txn.get(f"idx".encode())))
-print(data_list[0].keys())
-```
-You will get:
-```python
-['forces',            # Forces in kcal/mol/Angstrom
- 'cluster_ids',       # The id for each atoms in prebuilt clusters
- 'order',             # The frame id in MD simulation
- 'pos',               # The positons for each atoms, shape [N, 3], unit in Angstrom
- 'cluster_centers',   # The center of prebuilt clusters
- 'energy',            # The total energy of the molecule in kcal/mol
- 'atomic_numbers']    # The list of atomic numbers, shape [N,]
-```
-For Di-Molecule dataset, there are additional properties:
-```python
-[...
- 'mol_a',             # The pubchem id of the molecule A
- 'mol_b',             # The pubchem id of the molecule B
- 'distance',          # The horizontal distance between the right-most atom of A and the left-most atom of B, unit in Angstrom
-...]
+
+# Open the LMDB environment
+env = lmdb.open("md_traj/train/NaCl/data_0.lmdb", readonly=True, lock=False, subdir=False)
+
+with env.begin() as txn:
+    length = txn.stat()['entries']
+    data_list = []
+    for idx in range(length):
+        byte_data = txn.get(f"{idx}".encode())
+        if byte_data:
+            data_list.append(pickle.loads(byte_data))
+
+# See the keys stored in data
+if data_list:
+    print(data_list[0].keys())
+
 ```
 
-More details can be found in the original paper.
+### Data Structure
+
+The loaded objects contain the following keys:
+
+* `forces`: Atomic forces with shape \[N,3\], in kcal/mol/Angstrom.
+* `cluster_ids`: IDs for each atom within the prebuilt clusters.
+* `order`: The frame index in the MD simulation.
+* `pos`: Atomic positions with shape \[N,3\], in units of Angstrom
+* `cluster_centers`: Geometric centers of the prebuilt clusters.
+* `energy`: Total molecular energy in kcal/mol/Angstrom.
+* `atomic_numbers`: List of atomic numbers with shape \[N\].
+
+For the **Di-Molecule** dataset, the following additional properties are included:
+
+* `mol_a`: The PubChem ID of molecule A.
+* `mol_b`: The PubChem ID of molecule B.
+* `distance`: The horizontal distance between the right-most atom of A and the left-most atom of B, in units of Angstrom.
 
-Code Link: [IQuestLab/E2Former](https://github.com/IQuestLab/E2Former).
+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).