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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
---
# 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.*
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