Create dataset card for LoGIC-Dataset

Add metadata, dataset description, citation information, license, and associated code DOI for the LoGIC-Dataset release.

README.md

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+---
+license: cc-by-4.0
+language:
+  - en
+pretty_name: LoGIC-Dataset
+tags:
+  - tpms
+  - metamaterials
+  - porous-structures
+  - mechanical-metamaterials
+  - finite-element-analysis
+  - computational-fluid-dynamics
+  - permeability
+  - stress-strain
+  - surrogate-modeling
+  - logic-design
+size_categories:
+  - 100<n<1K
+---
+
+# LoGIC-Dataset
+
+LoGIC-Dataset is a research dataset associated with the LoGIC-Design framework for heterogeneous triply periodic minimal surface (TPMS) architectures.
+
+The dataset supports data-driven performance prediction of heterogeneous TPMS porous architectures generated using low-dimensional control grids. It includes geometry-related data and simulation-derived labels for mechanical and transport property prediction.
+
+## Associated code
+
+The source code of the LoGIC-Design framework is publicly available at:
+
+https://github.com/pxy1118/LoGIC
+
+The archived software release is available on Zenodo:
+
+https://doi.org/10.5281/zenodo.20411608
+
+## Dataset description
+
+The dataset contains heterogeneous TPMS architectures generated using the LoGIC-Design framework. The framework encodes topology distribution, lattice orientation, and local density using compact 3 × 3 × 3 control grids.
+
+The dataset includes two main groups of simulation-derived labels:
+
+- Mechanical response data obtained from finite element analysis, including stress–strain response labels.
+- Transport property data obtained from computational fluid dynamics simulations, including permeability and pressure-drop-related labels.
+
+In the associated manuscript, the mechanical response dataset contains 200 heterogeneous TPMS structures, and the permeability dataset contains 600 heterogeneous TPMS structures.
+
+## Intended use
+
+This dataset is intended for:
+
+- Data-driven prediction of mechanical response in heterogeneous TPMS architectures.
+- Permeability and pressure-drop prediction for porous structures.
+- Benchmarking surrogate models for structure–property prediction.
+- Reproducible research on heterogeneous porous metamaterials.
+- Future inverse design and human–AI collaborative design of TPMS-based architectures.
+
+## Citation
+
+If you use this dataset, please cite the associated manuscript and the dataset DOI.
+
+Associated software DOI:
+
+https://doi.org/10.5281/zenodo.20411608
+
+Dataset DOI:
+
+[Insert the Hugging Face dataset DOI here after generation]
+
+## License
+
+This dataset is released under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
+
+## Contact
+
+Xiaoyue Pan  
+College of Information Engineering, Zhejiang University of Technology  
+ORCID: 0009-0005-6098-5571