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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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# High-throughput DFT calculations of formation energy, stability and oxygen vacancy formation energy of ABO3 perovskites |
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Dataset containing DFT-calculated formation energy and convex hull energies of 4914 perovskite oxides |
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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/h15n-7wu8](https://doi.org/10.18126/h15n-7wu8) |
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- **Year**: 2022 |
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- **Authors**: Emery, Antoine, Wolverton, Chris |
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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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| formula | input | Material composition | | |
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| atom a | input | Element on the A-site sublattice | | |
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| atom b | input | Element on the B-site sublattice | | |
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| lowest distortion | input | Structural category of perovskite structure type | | |
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| e_form | target | DFT-calculated formation energy, relative to eleme | eV/atom | |
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| e_hull | target | DFT-calculated convex hull energy | eV/atom | |
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| mu_b | input | Magnetic moment of the relaxed structure | Bohr magneton | |
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| vpa | input | Atomic volume of the relaxed structure | Ang^3 per atom | |
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| gap pbe | input | DFT-PBE calculated band gap | eV | |
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| a | input | Lattice constant of the a-axis | Angstrom | |
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| b | input | Lattice constant of the b-axis | Angstrom | |
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| c | input | Lattice constant of the c-axis | Angstrom | |
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| alpha | input | Lattice alpha angle | degrees | |
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| beta | input | Lattice beta angle | degrees | |
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| gamma | input | Lattice gamma angle | degrees | |
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| e_form oxygen | target | Oxygen vacancy formation energy | eV | |
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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/h15n-7wu8") |
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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("wolverton_oxides_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/h15n-7wu8 |
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doi = {10.18126/h15n-7wu8} |
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url = {https://doi.org/10.18126/h15n-7wu8} |
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author = {Emery, Antoine and Wolverton, Chris} |
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title = {High-throughput DFT calculations of formation energy, stability and oxygen vacancy formation energy of ABO3 perovskites} |
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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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