Update README.md

README.md

@@ -65,20 +65,22 @@ dataset_info:
       sequence: 
         dtype: float32
 ---
+# 📊 Metamaterial MetaModulus Dataset
 
-# Dataset Summary
+## 🧾 Dataset Summary
 
-This dataset contains metamaterial lattice structure data for predicting modulus properties (i.e., Young's modulus, Shear's modulus, and Poisson's ratio), preprocessed into PyTorch Geometric (PyG) compatible format.
+This dataset contains 3D metamaterial lattice structures for predicting mechanical modulus properties, including **Young's modulus**, **Shear modulus**, and **Poisson's ratio**. Each sample is preprocessed into a format compatible with **[PyTorch Geometric (PyG)](https://pytorch-geometric.readthedocs.io/en/latest/)** for downstream machine learning tasks such as structure-property prediction, graph representation learning, and lattice optimization.
 
-# Dataset Usage
+---
+
+## 📦 Dataset Usage
 
-It's easy to download and transfer to PyG format using the following steps:
+You can easily download and convert the dataset into `torch_geometric.data.Data` objects using the following steps.
 
-**Step 1: Download**
-~~~
+### **Step 1: Load and Convert to PyG Format**
+```python
 from datasets import load_dataset
 from torch_geometric.data import Data
-from torch_geometric.loader import DataLoader
 import torch
 
 dataset = load_dataset("cjpcool/metamaterial-MetaModulus", split="full")
@@ -103,69 +105,78 @@ pyg_data_list = [
     )
     for d in dataset
 ]
-~~~
+```
 
-**Step 2: Split data to train/valid/test sets**
-~~~
+### **Step 2: Create Train/Valid/Test Splits**
+```python
 from sklearn.utils import shuffle
 
 def get_idx_split(data_size, train_size=8000, valid_size=2000, seed=42):
     ids = shuffle(range(data_size), random_state=seed)
-    train_idx, val_idx, test_idx = torch.LongTensor(ids[:train_size]), torch.tensor(
-        ids[train_size:train_size + valid_size]), torch.tensor(ids[train_size + valid_size:])
-    split_dict = {'train': train_idx, 'valid': val_idx, 'test': test_idx}
-    return split_dict
-split = get_idx_split(len(dataset), seed=42)
+    train_idx = torch.LongTensor(ids[:train_size])
+    val_idx = torch.LongTensor(ids[train_size:train_size + valid_size])
+    test_idx = torch.LongTensor(ids[train_size + valid_size:])
+    return {'train': train_idx, 'valid': val_idx, 'test': test_idx}
 
+split = get_idx_split(len(dataset), seed=42)
 train_data = [pyg_data_list[i] for i in split["train"]]
 valid_data = [pyg_data_list[i] for i in split["valid"]]
 test_data = [pyg_data_list[i] for i in split["test"]]
-~~~
+```
 
-**Step 3: Dataloader**
-~~~
-train_loader = DataLoader(train_data, batch_size=batch_size, shuffle=True)
-valid_loader = DataLoader(valid_data, batch_size=batch_size, shuffle=False)
-test_loader = DataLoader(test_data, batch_size=batch_size, shuffle=False)
-~~~
+### **Step 3: Create PyG Dataloaders**
+```python
+from torch_geometric.loader import DataLoader
 
+train_loader = DataLoader(train_data, batch_size=32, shuffle=True)
+valid_loader = DataLoader(valid_data, batch_size=32, shuffle=False)
+test_loader  = DataLoader(test_data, batch_size=32, shuffle=False)
+```
 
-# Dataset Sources 
+---
 
+## 📚 Dataset Source
 
-- **Repository:** https://github.com/cjpcool/Metamaterial-Benchmark
-- **Paper:** MetamatBench: Integrating Heterogeneous Data, Computational Tools, and Visual Interface for Metamaterial Discovery
+- **Repository:** [github.com/cjpcool/Metamaterial-Benchmark](https://github.com/cjpcool/Metamaterial-Benchmark)  
+- **Paper:** *MetamatBench: Integrating Heterogeneous Data, Computational Tools, and Visual Interface for Metamaterial Discovery*, KDD 2025 (Datasets and Benchmarks Track)
 
+---
 
-# Citation
+## 📌 Citation
 
-We greatly appreciate it if you can cite the dataset via the following:
+If you use this dataset in your research, please cite the following paper:
 
+```bibtex
 @inproceedings{metamatBench,
-  author={Chen, Jianpeng and Zhan, Wangzhi and Wang, Haohui and Jia, Zian and Gan, Jingru and Zhang, Junkai and Qi, Jingyuan and Chen, Tingwei and Huang, Lifu and Chen, Muhao and others},
+  author={Chen, Jianpeng and Zhan, Wangzhi and Wang, Haohui and Jia, Zian and Gan, Jingru and Zhang, Junkai and Qi, Jingyuan and Chen, Tingwei and Huang, Lifu and Chen, Muhao and Li, Ling and Wang, Wei and Zhou, Dawei},
   title     = {MetamatBench: Integrating Heterogeneous Data, Computational Tools, and Visual Interface for Metamaterial Discovery},
   booktitle = {Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)},
   year      = {2025},
   publisher = {ACM},
   doi       = {10.1145/3711896.3737416},
-  url       = {https://doi.org/10.1145/3711896.3737416}
 }
-
-## Raw Data
-
-In addition, we thank the raw dataset producers:
-
-@article{Modulus,
-  title={Exploring the property space of periodic cellular structures based on crystal networks},
-  author={Lumpe, Thomas S and Stankovic, Tino},
-  journal={Proceedings of the National Academy of Sciences},
-  volume={118},
-  number={7},
-  pages={e2003504118},
-  year={2021},
-  publisher={National Acad Sciences}
+```
+
+### 🧪 Raw Data Source
+
+We acknowledge the original data creators:
+
+```bibtex
+@article{2021lumpeExploring,
+  title     = {Exploring the property space of periodic cellular structures based on crystal networks},
+  author    = {Lumpe, Thomas S and Stankovic, Tino},
+  journal   = {Proceedings of the National Academy of Sciences},
+  volume    = {118},
+  number    = {7},
+  pages     = {e2003504118},
+  year      = {2021},
+  publisher = {National Acad Sciences}
 }
+```
+
+---
 
-## Dataset Card Contact
+## 📬 Contact
 
-Please contact cjpcool[at]outlook[dot]com if you have any questions about this dataset.
+For questions, feedback, or contributions, please reach out to:  
+📧 `cjpcool [at] outlook [dot] com`