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--- |
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license: cc-by-4.0 |
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task_categories: |
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- tabular-regression |
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- tabular-classification |
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tags: |
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- materials-science |
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- chemistry |
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- foundry-ml |
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- scientific-data |
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size_categories: |
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- 1K<n<10K |
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--- |
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# Charting the complete elastic properties of inorganic crystalline compounds |
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Dataset containing DFT-calculated elastic properties for 1181 materials |
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## Dataset Information |
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- **Source**: [Foundry-ML](https://github.com/MLMI2-CSSI/foundry) |
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- **DOI**: [10.18126/9fg1-528u](https://doi.org/10.18126/9fg1-528u) |
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- **Year**: 2022 |
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- **Authors**: de Jong, Maarten, Chen, Wei, Angsten, Thomas, Jain, Anubhav, Notestine, Randy, Gamst, Anthong, Sluiter, Marcel, Ande, Chaitanya Krishna, van der Zwaag, Sybrand, Plata, Jose J., Toher, Cormac, Curtarolo, Stefano, Ceder, Gerbrand, Persson, Kristin A., Asta, Mark |
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- **Data Type**: tabular |
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### Fields |
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| Field | Role | Description | Units | |
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|-------|------|-------------|-------| |
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| material_id | input | Materials Project ID | | |
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| formula | input | Material composition | | |
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| nsites | input | Number of sites in the unit cell | | |
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| space_group | input | Space group number | | |
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| volume | input | Volume of relaxed structure | Cubic Angstroms | |
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| structure | input | Pymatgen structure representation of material | | |
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| elastic_anisotropy | target | Description of elastic anisotropy | | |
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| G_Reuss | target | Shear modulus, lower bound for polycrystal | GPa | |
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| G_VRH | target | Average shear modulus | GPa | |
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| G_Voigt | target | Shear modulus, upper bound for polycrystal | GPa | |
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| K_Reuss | target | Bulk modulus, lower bound for polycrystal | GPa | |
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| K_VRH | target | Average bulk modulus | GPa | |
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| K_Voigt | target | Bulk modulus, upper bound for polycrystal | GPa | |
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| poisson_ratio | target | Describes lateral response to loading | | |
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| compliance_tensor | target | Tensor, describing elastic behavior | GPa | |
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| elastic_tensor | target | Tensor, describing elastic behavior in IEEE-format | GPa | |
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| elastic_tensor_original | target | Tensor, describing elastic behavior, corresponding | GPa | |
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| cif | input | Material structure in CIF format | | |
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| kpoint_density | N/A | K-point density used in DFT calculation | | |
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| poscar | input | Material structure in POSCAR format | | |
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### Splits |
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- **train**: train |
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## Usage |
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### With Foundry-ML (recommended for materials science workflows) |
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```python |
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from foundry import Foundry |
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f = Foundry() |
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dataset = f.get_dataset("10.18126/9fg1-528u") |
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X, y = dataset.get_as_dict()['train'] |
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``` |
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### With HuggingFace Datasets |
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```python |
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from datasets import load_dataset |
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dataset = load_dataset("elastic_tensor_v1.1") |
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``` |
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## Citation |
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```bibtex |
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@misc{https://doi.org/10.18126/9fg1-528u |
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doi = {10.18126/9fg1-528u} |
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url = {https://doi.org/10.18126/9fg1-528u} |
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author = {de Jong, Maarten and Chen, Wei and Angsten, Thomas and Jain, Anubhav and Notestine, Randy and Gamst, Anthong and Sluiter, Marcel and Ande, Chaitanya Krishna and van der Zwaag, Sybrand and Plata, Jose J. and Toher, Cormac and Curtarolo, Stefano and Ceder, Gerbrand and Persson, Kristin A. and Asta, Mark} |
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title = {Charting the complete elastic properties of inorganic crystalline compounds} |
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keywords = {machine learning, foundry} |
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publisher = {Materials Data Facility} |
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year = {root=2022}} |
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``` |
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## License |
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CC-BY 4.0 |
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--- |
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*This dataset was exported from [Foundry-ML](https://github.com/MLMI2-CSSI/foundry), a platform for materials science datasets.* |
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