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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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# Data for: Ab initio control of zeolite synthesis and intergrowth with high-throughput simulations |
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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/c5z9-zej7](https://doi.org/10.18126/c5z9-zej7) |
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- **Year**: 2021 |
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- **Authors**: Schwalbe-Koda, Daniel, Gómez-Bombarelli, Rafael |
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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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| crystal_id | input | unique identifier associated with each pose. It is | | |
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| Zeolite | input | IZA code of the zeolite | | |
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| SMILES | input | SMILES string of the guest docked in the zeolite | | |
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| InchiKey | input | InchiKey of the guest docked in the zeolite | | |
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| Ligand formula | input | formula of one molecular guest | | |
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| Loading | input | number of OSDAs per unit cell in the calculated po | | |
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| Binding (SiO2) | target | binding energy between the host and the guest, cal | kJ/mol | |
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| Binding (OSDA) | target | binding energy between the host and the guest, cal | | |
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| Directivity (SiO2) | target | binding energy between the host and the guest, usi | kJ/mol | |
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| Competition (SiO2) | target | competition energy between different hosts for a g | kJ/mol | |
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| Competition (OSDA) | target | competition energy between different hosts for a g | kJ/mol | |
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| Templating | target | templating energy at 400 K, as calculated in the p | kJ/mol | |
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| SCScore | input | Synthetic Complexity Score, as proposed by Coley e | kJ/mol | |
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| Volume (Angstrom3) | input | volume of the OSDA, given in Angstrom^3. | Angstrom^3 | |
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| Axis 1 (Angstrom) | input | first principal component of the OSDA, given in An | Angstrom | |
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| Axis 2 (Angstrom) | input | second principal component of the OSDA, given in A | Angstrom | |
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| In literature? | input | If the pair is known in the literature, the value | kJ/mol | |
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| lattice | input | lattice matrix of the crystal | | |
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| nxyz | input | tuple containing the atomic number and the (x, y, | | |
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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/c5z9-zej7") |
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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("foundry_osdb_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/c5z9-zej7 |
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doi = {10.18126/c5z9-zej7} |
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url = {https://doi.org/10.18126/c5z9-zej7} |
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author = {Schwalbe-Koda, Daniel and Gómez-Bombarelli, Rafael} |
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title = {Data for: Ab initio control of zeolite synthesis and intergrowth with high-throughput simulations} |
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keywords = {machine learning, foundry, zeolite, database} |
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publisher = {Materials Data Facility} |
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year = {root=2021}} |
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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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