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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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# Machine learning for impurity charge-state transition levels in semiconductors from elemental properties using multi-fidelity datasets |
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Dataset containing DFT-calculated defect charge state transition levels of 2910 semiconductor-impurity pairs |
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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/ite0-3iah](https://doi.org/10.18126/ite0-3iah) |
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- **Year**: 2022 |
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- **Authors**: Polak, Maciej P., Jacobs, Ryan, Mannodi-Kanakkithodi, Arun, Chan, Maria K. Y., Morgan, Dane |
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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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| host_material | input | Composition of host | | |
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| host_element_a | input | Host element of A site | | |
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| host_element_b | input | Host element of B site | | |
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| impurity | input | Impurity element type | | |
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| removed_element | input | Removed element type | | |
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| site_type | input | Type of the lattice site the impurity resides | | |
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| site | input | Name of the lattice site the impurity resides | | |
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| is_a_latt | input | Whether impurity resides on the A sublattice | | |
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| is_interstitial | input | Whether impurity is on an interstitial site | | |
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| is_interstitial_a | input | Whether impurity is on a A-site interstitial site | | |
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| is_interstitial_b | input | Whether impurity is on a B-site interstitial site | | |
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| is_interstitial_n | input | Whether impurity is on a neutral interstitial site | | |
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| M_Al | input | One-hot encoding of site type | | |
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| M_As | input | One-hot encoding of site type | | |
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| M_Cd | input | One-hot encoding of site type | | |
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| M_Ga | input | One-hot encoding of site type | | |
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| M_In | input | One-hot encoding of site type | | |
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| M_P | input | One-hot encoding of site type | | |
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| M_S | input | One-hot encoding of site type | | |
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| M_Sb | input | One-hot encoding of site type | | |
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| M_Se | input | One-hot encoding of site type | | |
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| M_Te | input | One-hot encoding of site type | | |
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| M_i_Cd_site | input | One-hot encoding of site type | | |
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| M_i_S_site | input | One-hot encoding of site type | | |
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| M_i_Se_site | input | One-hot encoding of site type | | |
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| M_i_Te_site | input | One-hot encoding of site type | | |
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| M_i_neut_site | input | One-hot encoding of site type | | |
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| charge_from | input | Initial charge of defect | electrons | |
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| charge_to | input | Final charge of defect | electrons | |
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| host bandgap_[eV] | input | Experimental bandgap of the host material | eV | |
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| host lattice constant_[Ang.] | input | Lattice constant of the host material | Angstroms | |
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| host_epsilon | input | Dielectric constant of the host material | | |
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| ba_shift | input | Band alignment correction shift | eV | |
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| pbe defect level (relative to VBM)_[eV] | target | DFT-PBE calculated defect charge state transition | eV | |
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| mba_pbe | input | Modified band alignment correction shift | eV | |
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| mba_pbe_gapfrac | input | Modified band alignment correction shift, given as | | |
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| hse defect level (relative to VBM)_[eV] | target | DFT-HSE calculated defect charge state transition | 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/ite0-3iah") |
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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("semiconductor_defectlevels_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/ite0-3iah |
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doi = {10.18126/ite0-3iah} |
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url = {https://doi.org/10.18126/ite0-3iah} |
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author = {Polak, Maciej P. and Jacobs, Ryan and Mannodi-Kanakkithodi, Arun and Chan, Maria K. Y. and Morgan, Dane} |
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title = {Machine learning for impurity charge-state transition levels in semiconductors from elemental properties using multi-fidelity datasets} |
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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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