Upload README.md with huggingface_hub

README.md

@@ -1,76 +1,86 @@
 ---
-dataset_info:
-  features:
-  - name: reference
-    dtype: string
-  - name: icsd_id
-    sequence: int64
-  - name: structure
-    struct:
-    - name: '@class'
-      dtype: string
-    - name: '@module'
-      dtype: string
-    - name: charge
-      dtype: 'null'
-    - name: lattice
-      struct:
-      - name: a
-        dtype: float64
-      - name: alpha
-        dtype: float64
-      - name: b
-        dtype: float64
-      - name: beta
-        dtype: float64
-      - name: c
-        dtype: float64
-      - name: gamma
-        dtype: float64
-      - name: matrix
-        sequence:
-          sequence: float64
-      - name: volume
-        dtype: float64
-    - name: sites
-      list:
-      - name: abc
-        sequence: float64
-      - name: label
-        dtype: string
-      - name: properties
-        struct:
-        - name: magmom
-          dtype: float64
-      - name: species
-        list:
-        - name: element
-          dtype: string
-        - name: occu
-          dtype: int64
-      - name: xyz
-        sequence: float64
-  - name: composition
-    dtype: string
-  - name: comments
-    dtype: string
-  - name: bandgap type
-    dtype: string
-  - name: comp method
-    dtype: string
-  - name: space group
-    dtype: string
-  - name: bandgap value (eV)
-    dtype: float64
-  splits:
-  - name: train
-    num_bytes: 168430067
-    num_examples: 74992
-  download_size: 85394436
-  dataset_size: 168430067
-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
 ---
+
+# Graph Network Based Deep Learning of Band Gaps - Materials Project PBE Band Gaps
+
+
+
+## Dataset Information
+
+- **Source**: [Foundry-ML](https://github.com/MLMI2-CSSI/foundry)
+- **DOI**: [10.18126/vjwr-5bs9](https://doi.org/10.18126/vjwr-5bs9)
+- **Year**: 2021
+- **Authors**: Li, Xiang-Guo, Blaiszik, Ben, Schwarting, Marcus, Jacobs, Ryan, Scourtas, Aristana, Schmidt, KJ, Voyles, Paul, Morgan, Dane
+- **Data Type**: tabular
+
+### Fields
+
+| Field | Role | Description | Units |
+|-------|------|-------------|-------|
+| reference | input | source publication of the band gap value |  |
+| icsd_id | input | corresponding id in ICSD of this compound |  |
+| structure | input | the structure of this compound |  |
+| composition | input | reduced composition of this compound |  |
+| comments | input | Additional information about this bandgap measurem |  |
+| bandgap type | input | the type of the band gap, e.g., direct or indirect |  |
+| comp method | input | functional used to calculate the band gap |  |
+| space group | input | the space group of this compound |  |
+| bandgap value (eV) | target | value of the band gap | 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/vjwr-5bs9")
+X, y = dataset.get_as_dict()['train']
+```
+
+### With HuggingFace Datasets
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("foundry_mp_band_gaps_v1.1")
+```
+
+## Citation
+
+```bibtex
+@misc{https://doi.org/10.18126/vjwr-5bs9
+doi = {10.18126/vjwr-5bs9}
+url = {https://doi.org/10.18126/vjwr-5bs9}
+author = {Li, Xiang-Guo and Blaiszik, Ben and Schwarting, Marcus and Jacobs, Ryan and Scourtas, Aristana and Schmidt, KJ and Voyles, Paul and Morgan, Dane}
+title = {Graph Network Based Deep Learning of Band Gaps - Materials Project PBE Band Gaps}
+keywords = {machine learning, foundry, band gap, neural network}
+publisher = {Materials Data Facility}
+year = {root=2021}}
+```
+
+## License
+
+CC-BY 4.0
+
+---
+
+*This dataset was exported from [Foundry-ML](https://github.com/MLMI2-CSSI/foundry), a platform for materials science datasets.*