Datasets:

cjpcool
/

metamaterial-MetaModulus

@@ -66,140 +66,106 @@ dataset_info:
         dtype: float32
 ---
-# Dataset Card for Dataset Name
-<!-- Provide a quick summary of the dataset. -->
-This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
-## Dataset Details
-### Dataset Description
-<!-- Provide a longer summary of what this dataset is. -->
-- **Curated by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-### Dataset Sources [optional]
-<!-- Provide the basic links for the dataset. -->
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-## Uses
-<!-- Address questions around how the dataset is intended to be used. -->
-### Direct Use
-<!-- This section describes suitable use cases for the dataset. -->
-[More Information Needed]
-### Out-of-Scope Use
-<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
-[More Information Needed]
-## Dataset Structure
-<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
-[More Information Needed]
-## Dataset Creation
-### Curation Rationale
-<!-- Motivation for the creation of this dataset. -->
-[More Information Needed]
-### Source Data
-<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
-#### Data Collection and Processing
-<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
-[More Information Needed]
-#### Who are the source data producers?
-<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
-[More Information Needed]
-### Annotations [optional]
-<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
-#### Annotation process
-<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
-[More Information Needed]
-#### Who are the annotators?
-<!-- This section describes the people or systems who created the annotations. -->
-[More Information Needed]
-#### Personal and Sensitive Information
-<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
-[More Information Needed]
-## Bias, Risks, and Limitations
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-[More Information Needed]
-### Recommendations
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
-## Citation [optional]
-<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
-**BibTeX:**
-[More Information Needed]
-**APA:**
-[More Information Needed]
-## Glossary [optional]
-<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
-[More Information Needed]
-## More Information [optional]
-[More Information Needed]
-## Dataset Card Authors [optional]
-[More Information Needed]
 ## Dataset Card Contact
-[More Information Needed]

         dtype: float32
 ---
+# 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.
+# Dataset Usage
+It's easy to download and transfer to PyG format using the following steps:
+**Step 1: Download**
+~~~
+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")
+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: Split data to train/valid/test sets**
+~~~
+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_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)
+~~~
+# Dataset Sources
+- **Repository:** https://github.com/cjpcool/Metamaterial-Benchmark
+- **Paper:** MetamatBench: Integrating Heterogeneous Data, Computational Tools, and Visual Interface for Metamaterial Discovery
+# Citation
+We greatly appreciate it if you can cite the dataset via the following:
+@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},
+  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}
+}
 ## Dataset Card Contact
+Please contact cjpcool[at]outlook[dot]com if you have any questions about this dataset.