README.md

---
license: mit
task_categories:
- graph-ml
tags:
- material
- metamaterial
- graph
- Geospatial
- arxiv:2505.20299
size_categories:
- 10K<n<100K
configs:
- config_name: default
  data_files:
  - split: full
    path: data/processed/data.parquet
dataset_info:
  features:
  - name: frac_coords
    sequence:
      sequence:
        dtype: float32
  - name: cart_coords
    sequence:
      sequence:
        dtype: float32
  - name: node_feat
    sequence:
      sequence:
        dtype: float32
  - name: node_type
    sequence:
      dtype: int64
  - name: edge_feat
    sequence:
      sequence:
        dtype: float32
  - name: edge_index
    sequence:
      sequence:
        dtype: int64
  - name: lengths
    sequence:
      dtype: float32
  - name: num_nodes
    dtype: int64
  - name: num_atoms
    dtype: int64
  - name: angles
    sequence:
      dtype: float32
  - name: vector
    sequence:
      dtype: float32
  - name: 'y'
    sequence:
      dtype: float32
  - name: young
    sequence:
      dtype: float32
  - name: shear
    sequence:
      dtype: float32
  - name: poisson
    sequence:
      dtype: float32
---
# 📊 Metamaterial MetaModulus Dataset

## 🧾 Dataset Summary

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.

Check our paper on [arxiv.org/abs/2505.20299](https://arxiv.org/abs/2505.20299) for more details.

---

## 📦 Dataset Usage

You can easily download and convert the dataset into `torch_geometric.data.Data` objects using the following steps.

### **Step 1: Load and Convert to PyG Format**
```python
from datasets import load_dataset
from torch_geometric.data import Data
import torch

dataset = load_dataset("cjpcool/metamaterial-MetaModulus", split="full")

pyg_data_list = [
    Data(
        frac_coords=torch.tensor(d["frac_coords"]),
        cart_coords=torch.tensor(d["cart_coords"]),
        node_feat=torch.tensor(d["node_feat"]),
        node_type=torch.tensor(d["node_type"]),
        edge_feat=torch.tensor(d["edge_feat"]),
        edge_index=torch.tensor(d["edge_index"]),
        lengths=torch.tensor(d["lengths"]).unsqueeze(0),
        angles=torch.tensor(d["angles"]).unsqueeze(0),
        vector=torch.tensor(d["vector"]).unsqueeze(0),
        y=torch.tensor(d["y"]).unsqueeze(0),
        young=torch.tensor(d["young"]).unsqueeze(0),
        shear=torch.tensor(d["shear"]).unsqueeze(0),
        poisson=torch.tensor(d["poisson"]).unsqueeze(0),
        num_nodes=d["num_nodes"],
        num_atoms=d["num_atoms"]
    )
    for d in dataset
]
```

### **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 = 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: 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 Source

- **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

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 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 31th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)},
  year      = {2025},
  publisher = {ACM},
  doi       = {10.1145/3711896.3737416},
}
```

### 🧪 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}
}
```

---

## 📬 Contact

For questions, feedback, or contributions, please reach out to:  
📧 `cjpcool [at] outlook [dot] com`