Upload README.md with huggingface_hub

README.md

@@ -1,55 +1,96 @@
 ---
-dataset_info:
-  features:
-  - name: crystal_id
-    dtype: int64
-  - name: Zeolite
-    dtype: string
-  - name: SMILES
-    dtype: string
-  - name: InchiKey
-    dtype: string
-  - name: Ligand formula
-    dtype: string
-  - name: Loading
-    dtype: int64
-  - name: SCScore
-    dtype: float64
-  - name: Volume (Angstrom3)
-    dtype: float64
-  - name: Axis 1 (Angstrom)
-    dtype: float64
-  - name: Axis 2 (Angstrom)
-    dtype: float64
-  - name: In literature?
-    dtype: int64
-  - name: lattice
-    sequence:
-      sequence: float64
-  - name: nxyz
-    sequence:
-      sequence: float64
-  - name: Binding (SiO2)
-    dtype: float64
-  - name: Binding (OSDA)
-    dtype: float64
-  - name: Directivity (SiO2)
-    dtype: float64
-  - name: Competition (SiO2)
-    dtype: float64
-  - name: Competition (OSDA)
-    dtype: float64
-  - name: Templating
-    dtype: float64
-  splits:
-  - name: train
-    num_bytes: 1218526382
-    num_examples: 112425
-  download_size: 904434024
-  dataset_size: 1218526382
-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
 ---
+
+# Data for: Ab initio control of zeolite synthesis and intergrowth with high-throughput simulations
+
+
+
+## Dataset Information
+
+- **Source**: [Foundry-ML](https://github.com/MLMI2-CSSI/foundry)
+- **DOI**: [10.18126/c5z9-zej7](https://doi.org/10.18126/c5z9-zej7)
+- **Year**: 2021
+- **Authors**: Schwalbe-Koda, Daniel, Gómez-Bombarelli, Rafael
+- **Data Type**: tabular
+
+### Fields
+
+| Field | Role | Description | Units |
+|-------|------|-------------|-------|
+| crystal_id | input | unique identifier associated with each pose. It is |  |
+| Zeolite | input | IZA code of the zeolite |  |
+| SMILES | input |  SMILES string of the guest docked in the zeolite |  |
+| InchiKey | input | InchiKey of the guest docked in the zeolite |  |
+| Ligand formula | input | formula of one molecular guest |  |
+| Loading | input | number of OSDAs per unit cell in the calculated po |  |
+| Binding (SiO2) | target | binding energy between the host and the guest, cal | kJ/mol |
+| Binding (OSDA) | target | binding energy between the host and the guest, cal |  |
+| Directivity (SiO2) | target | binding energy between the host and the guest, usi | kJ/mol |
+| Competition (SiO2) | target | competition energy between different hosts for a g | kJ/mol |
+| Competition (OSDA) | target | competition energy between different hosts for a g | kJ/mol |
+| Templating | target | templating energy at 400 K, as calculated in the p | kJ/mol |
+| SCScore | input | Synthetic Complexity Score, as proposed by Coley e | kJ/mol |
+| Volume (Angstrom3) | input | volume of the OSDA, given in Angstrom^3. | Angstrom^3 |
+| Axis 1 (Angstrom) | input | first principal component of the OSDA, given in An | Angstrom |
+| Axis 2 (Angstrom) | input | second principal component of the OSDA, given in A | Angstrom |
+| In literature? | input | If the pair is known in the literature, the value  | kJ/mol |
+| lattice | input | lattice matrix of the crystal |  |
+| nxyz | input | tuple containing the atomic number and the (x, y,  |  |
+
+
+### 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/c5z9-zej7")
+X, y = dataset.get_as_dict()['train']
+```
+
+### With HuggingFace Datasets
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("foundry_osdb_v1.1")
+```
+
+## Citation
+
+```bibtex
+@misc{https://doi.org/10.18126/c5z9-zej7
+doi = {10.18126/c5z9-zej7}
+url = {https://doi.org/10.18126/c5z9-zej7}
+author = {Schwalbe-Koda, Daniel and Gómez-Bombarelli, Rafael}
+title = {Data for: Ab initio control of zeolite synthesis and intergrowth with high-throughput simulations}
+keywords = {machine learning, foundry, zeolite, database}
+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.*