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---
license: cc-by-4.0
task_categories:
- tabular-regression
- tabular-classification
tags:
- materials-science
- chemistry
- foundry-ml
- scientific-data
size_categories:
- 1K<n<10K
---
# Machine learning for impurity charge-state transition levels in semiconductors from elemental properties using multi-fidelity datasets
Dataset containing DFT-calculated defect charge state transition levels of 2910 semiconductor-impurity pairs
## Dataset Information
- **Source**: [Foundry-ML](https://github.com/MLMI2-CSSI/foundry)
- **DOI**: [10.18126/ite0-3iah](https://doi.org/10.18126/ite0-3iah)
- **Year**: 2022
- **Authors**: Polak, Maciej P., Jacobs, Ryan, Mannodi-Kanakkithodi, Arun, Chan, Maria K. Y., Morgan, Dane
- **Data Type**: tabular
### Fields
| Field | Role | Description | Units |
|-------|------|-------------|-------|
| host_material | input | Composition of host | |
| host_element_a | input | Host element of A site | |
| host_element_b | input | Host element of B site | |
| impurity | input | Impurity element type | |
| removed_element | input | Removed element type | |
| site_type | input | Type of the lattice site the impurity resides | |
| site | input | Name of the lattice site the impurity resides | |
| is_a_latt | input | Whether impurity resides on the A sublattice | |
| is_interstitial | input | Whether impurity is on an interstitial site | |
| is_interstitial_a | input | Whether impurity is on a A-site interstitial site | |
| is_interstitial_b | input | Whether impurity is on a B-site interstitial site | |
| is_interstitial_n | input | Whether impurity is on a neutral interstitial site | |
| M_Al | input | One-hot encoding of site type | |
| M_As | input | One-hot encoding of site type | |
| M_Cd | input | One-hot encoding of site type | |
| M_Ga | input | One-hot encoding of site type | |
| M_In | input | One-hot encoding of site type | |
| M_P | input | One-hot encoding of site type | |
| M_S | input | One-hot encoding of site type | |
| M_Sb | input | One-hot encoding of site type | |
| M_Se | input | One-hot encoding of site type | |
| M_Te | input | One-hot encoding of site type | |
| M_i_Cd_site | input | One-hot encoding of site type | |
| M_i_S_site | input | One-hot encoding of site type | |
| M_i_Se_site | input | One-hot encoding of site type | |
| M_i_Te_site | input | One-hot encoding of site type | |
| M_i_neut_site | input | One-hot encoding of site type | |
| charge_from | input | Initial charge of defect | electrons |
| charge_to | input | Final charge of defect | electrons |
| host bandgap_[eV] | input | Experimental bandgap of the host material | eV |
| host lattice constant_[Ang.] | input | Lattice constant of the host material | Angstroms |
| host_epsilon | input | Dielectric constant of the host material | |
| ba_shift | input | Band alignment correction shift | eV |
| pbe defect level (relative to VBM)_[eV] | target | DFT-PBE calculated defect charge state transition | eV |
| mba_pbe | input | Modified band alignment correction shift | eV |
| mba_pbe_gapfrac | input | Modified band alignment correction shift, given as | |
| hse defect level (relative to VBM)_[eV] | target | DFT-HSE calculated defect charge state transition | eV |
### Splits
- **train**: train
## Usage
### With Foundry-ML (recommended for materials science workflows)
```python
from foundry import Foundry
f = Foundry()
dataset = f.get_dataset("10.18126/ite0-3iah")
X, y = dataset.get_as_dict()['train']
```
### With HuggingFace Datasets
```python
from datasets import load_dataset
dataset = load_dataset("semiconductor_defectlevels_v1.1")
```
## Citation
```bibtex
@misc{https://doi.org/10.18126/ite0-3iah
doi = {10.18126/ite0-3iah}
url = {https://doi.org/10.18126/ite0-3iah}
author = {Polak, Maciej P. and Jacobs, Ryan and Mannodi-Kanakkithodi, Arun and Chan, Maria K. Y. and Morgan, Dane}
title = {Machine learning for impurity charge-state transition levels in semiconductors from elemental properties using multi-fidelity datasets}
keywords = {machine learning, foundry}
publisher = {Materials Data Facility}
year = {root=2022}}
```
## License
CC-BY 4.0
---
*This dataset was exported from [Foundry-ML](https://github.com/MLMI2-CSSI/foundry), a platform for materials science datasets.*
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