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--- |
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license: other |
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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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# Machine-learning prediction of thermal expansion coefficient for perovskite oxides with experimental validation |
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Dataset containing 137 perovskite thermal expansion coefficients from experiment |
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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/avvr-p174](https://doi.org/10.18126/avvr-p174) |
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- **Year**: 2023 |
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- **Authors**: McGuinness, Kevin P., Oliynyk, Anton O., Lee, Sangjoon, Molero-Sanchez, Beatriz, Addo, Paul Kwesi |
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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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| Composition | input | Material composition with sites | | |
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| Initial Temperature (degC) | input | Initial temperature | degC | |
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| Final Temperature (degC) | input | Final temperature | degC | |
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| TEC (x10^-6 K^-1) | target | Thermal expansion coefficient | K^-1 | |
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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/avvr-p174") |
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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("Dataset_perovskite_tec") |
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``` |
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## Citation |
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```bibtex |
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@misc{https://doi.org/10.18126/avvr-p174 |
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doi = {10.18126/avvr-p174} |
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url = {https://doi.org/10.18126/avvr-p174} |
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author = {McGuinness, Kevin P. and Oliynyk, Anton O. and Lee, Sangjoon and Molero-Sanchez, Beatriz and Addo, Paul Kwesi} |
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title = {Machine-learning prediction of thermal expansion coefficient for perovskite oxides with experimental validation} |
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keywords = {machine learning, foundry} |
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publisher = {Materials Data Facility} |
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year = {root=2023}} |
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``` |
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## License |
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other |
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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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