Upload README.md with huggingface_hub

README.md

@@ -1,89 +1,114 @@
 ---
-dataset_info:
-  features:
-  - name: host_material
-    dtype: string
-  - name: host_element_a
-    dtype: string
-  - name: host_element_b
-    dtype: string
-  - name: impurity
-    dtype: string
-  - name: removed_element
-    dtype: string
-  - name: site_type
-    dtype: string
-  - name: site
-    dtype: string
-  - name: is_a_latt
-    dtype: int64
-  - name: is_interstitial
-    dtype: int64
-  - name: is_interstitial_a
-    dtype: int64
-  - name: is_interstitial_b
-    dtype: int64
-  - name: is_interstitial_n
-    dtype: int64
-  - name: M_Al
-    dtype: int64
-  - name: M_As
-    dtype: int64
-  - name: M_Cd
-    dtype: int64
-  - name: M_Ga
-    dtype: int64
-  - name: M_In
-    dtype: int64
-  - name: M_P
-    dtype: int64
-  - name: M_S
-    dtype: int64
-  - name: M_Sb
-    dtype: int64
-  - name: M_Se
-    dtype: int64
-  - name: M_Te
-    dtype: int64
-  - name: M_i_Cd_site
-    dtype: int64
-  - name: M_i_S_site
-    dtype: int64
-  - name: M_i_Se_site
-    dtype: int64
-  - name: M_i_Te_site
-    dtype: int64
-  - name: M_i_neut_site
-    dtype: int64
-  - name: charge_from
-    dtype: int64
-  - name: charge_to
-    dtype: int64
-  - name: host bandgap_[eV]
-    dtype: float64
-  - name: host lattice constant_[Ang.]
-    dtype: float64
-  - name: host_epsilon
-    dtype: float64
-  - name: ba_shift
-    dtype: float64
-  - name: mba_pbe
-    dtype: float64
-  - name: mba_pbe_gapfrac
-    dtype: float64
-  - name: pbe defect level (relative to VBM)_[eV]
-    dtype: float64
-  - name: hse defect level (relative to VBM)_[eV]
-    dtype: float64
-  splits:
-  - name: train
-    num_bytes: 846492
-    num_examples: 2910
-  download_size: 102805
-  dataset_size: 846492
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
+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.*