
# Dataset Card for ethanol

## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
  - [Dataset Summary](#dataset-summary)
  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [External Use](#external-use)
  - [PyGeometric](#pygeometric)
- [Dataset Structure](#dataset-structure)
  - [Data Properties](#data-properties)
  - [Data Fields](#data-fields)
  - [Data Splits](#data-splits)
- [Additional Information](#additional-information)
  - [Licensing Information](#licensing-information)
  - [Citation Information](#citation-information)
  - [Contributions](#contributions)

## Dataset Description
- **[Homepage](http://www.sgdml.org/#datasets)**
- **Paper:**:  (see citation)


### Dataset Summary
The `ethanol` dataset is  a molecular dynamics (MD) dataset. The total energy and force labels for each dataset were computed using the PBE+vdW-TS electronic structure method. All geometries are in Angstrom, energies and forces are given in kcal/mol and kcal/mol/A respectively.


### Supported Tasks and Leaderboards
`ethanol` should be used for organic molecular property prediction, a regression task on 1 property. The score used is Mean absolute errors (in meV) for energy prediction.


## External Use
### PyGeometric
To load in PyGeometric, do the following:

```python
from datasets import load_dataset

from torch_geometric.data import Data
from torch_geometric.loader import DataLoader

dataset_hf = load_dataset("graphs-datasets/<mydataset>")
# For the train set (replace by valid or test as needed)
dataset_pg_list = [Data(graph) for graph in dataset_hf["train"]]
dataset_pg = DataLoader(dataset_pg_list)
```

## Dataset Structure

### Data Properties
| property | value |
|---|---|
| scale | big |
| #graphs | 455092 |
| average #nodes | 9.0 |
| average #edges | 72.0 |

### Data Fields

Each row of a given file is a graph, with: 
- `node_feat` (list: #nodes x #node-features): nodes
- `edge_index` (list: 2 x #edges): pairs of nodes constituting edges
- `edge_attr` (list: #edges x #edge-features): for the aforementioned edges, contains their features
- `y` (list:  #labels): contains the number of labels available to predict
- `num_nodes` (int): number of nodes of the graph

### Data Splits

This data is not split, and should be used with cross validation. It comes from the PyGeometric version of the dataset.

## Additional Information

### Licensing Information
The dataset has been released under license unknown.

### Citation Information
Please cite both papers when using these datasets in publications.
```
@inproceedings{Morris+2020,
    title={TUDataset: A collection of benchmark datasets for learning with graphs},
    author={Christopher Morris and Nils M. Kriege and Franka Bause and Kristian Kersting and Petra Mutzel and Marion Neumann},
    booktitle={ICML 2020 Workshop on Graph Representation Learning and Beyond (GRL+ 2020)},
    archivePrefix={arXiv},
    eprint={2007.08663},
    url={www.graphlearning.io},
    year={2020}
}
```

```

@article{Chmiela_2017,
	doi = {10.1126/sciadv.1603015},
	url = {https://doi.org/10.1126%2Fsciadv.1603015},
	year = 2017,
	month = {may},
	publisher = {American Association for the Advancement of Science ({AAAS})},
	volume = {3},
	number = {5},
	author = {Stefan Chmiela and Alexandre Tkatchenko and Huziel E. Sauceda and Igor Poltavsky and Kristof T. Schütt and Klaus-Robert Müller},
	title = {Machine learning of accurate energy-conserving molecular force fields},
	journal = {Science Advances}
}


```
task_categories:graph-ml arxiv:2007.08663 region:us
{"task_categories": ["graph-ml"], "licence": "unknown"}
TAGS
#task_categories-graph-ml #arxiv-2007.08663 #region-us 

Dataset Card for ethanol
========================


Table of Contents
-----------------


* Table of Contents
* Dataset Description
	+ Dataset Summary
	+ Supported Tasks and Leaderboards
* External Use
	+ PyGeometric
* Dataset Structure
	+ Data Properties
	+ Data Fields
	+ Data Splits
* Additional Information
	+ Licensing Information
	+ Citation Information
	+ Contributions


Dataset Description
-------------------


* Homepage
* Paper:: (see citation)


### Dataset Summary


The 'ethanol' dataset is a molecular dynamics (MD) dataset. The total energy and force labels for each dataset were computed using the PBE+vdW-TS electronic structure method. All geometries are in Angstrom, energies and forces are given in kcal/mol and kcal/mol/A respectively.


### Supported Tasks and Leaderboards


'ethanol' should be used for organic molecular property prediction, a regression task on 1 property. The score used is Mean absolute errors (in meV) for energy prediction.


External Use
------------


### PyGeometric


To load in PyGeometric, do the following:


Dataset Structure
-----------------


### Data Properties



### Data Fields


Each row of a given file is a graph, with:


* 'node\_feat' (list: #nodes x #node-features): nodes
* 'edge\_index' (list: 2 x #edges): pairs of nodes constituting edges
* 'edge\_attr' (list: #edges x #edge-features): for the aforementioned edges, contains their features
* 'y' (list: #labels): contains the number of labels available to predict
* 'num\_nodes' (int): number of nodes of the graph


### Data Splits


This data is not split, and should be used with cross validation. It comes from the PyGeometric version of the dataset.


Additional Information
----------------------


### Licensing Information


The dataset has been released under license unknown.


Please cite both papers when using these datasets in publications.



### Dataset Summary


The 'ethanol' dataset is a molecular dynamics (MD) dataset. The total energy and force labels for each dataset were computed using the PBE+vdW-TS electronic structure method. All geometries are in Angstrom, energies and forces are given in kcal/mol and kcal/mol/A respectively. ### Supported Tasks and Leaderboards


'ethanol' should be used for organic molecular property prediction, a regression task on 1 property. The score used is Mean absolute errors (in meV) for energy prediction.


External Use
------------ ### PyGeometric


To load in PyGeometric, do the following:


Dataset Structure
----------------- ### Data Properties ### Data Fields


Each row of a given file is a graph, with:


* 'node\_feat' (list: #nodes x #node-features): nodes
* 'edge\_index' (list: 2 x #edges): pairs of nodes constituting edges
* 'edge\_attr' (list: #edges x #edge-features): for the aforementioned edges, contains their features
* 'y' (list: #labels): contains the number of labels available to predict
* 'num\_nodes' (int): number of nodes of the graph ### Data Splits


This data is not split, and should be used with cross validation. It comes from the PyGeometric version of the dataset.


Additional Information
---------------------- ### Licensing Information


The dataset has been released under license unknown.


Please cite both papers when using these datasets in publications.
TAGS
#task_categories-graph-ml #arxiv-2007.08663 #region-us 
 ### Dataset Summary


The 'ethanol' dataset is a molecular dynamics (MD) dataset. The total energy and force labels for each dataset were computed using the PBE+vdW-TS electronic structure method. All geometries are in Angstrom, energies and forces are given in kcal/mol and kcal/mol/A respectively. ### Supported Tasks and Leaderboards


'ethanol' should be used for organic molecular property prediction, a regression task on 1 property. The score used is Mean absolute errors (in meV) for energy prediction.


External Use
------------ ### PyGeometric


To load in PyGeometric, do the following:


Dataset Structure
----------------- ### Data Properties ### Data Fields


Each row of a given file is a graph, with:


* 'node\_feat' (list: #nodes x #node-features): nodes
* 'edge\_index' (list: 2 x #edges): pairs of nodes constituting edges
* 'edge\_attr' (list: #edges x #edge-features): for the aforementioned edges, contains their features
* 'y' (list: #labels): contains the number of labels available to predict
* 'num\_nodes' (int): number of nodes of the graph ### Data Splits


This data is not split, and should be used with cross validation. It comes from the PyGeometric version of the dataset.


Additional Information
---------------------- ### Licensing Information


The dataset has been released under license unknown.


Please cite both papers when using these datasets in publications.
passage: TAGS
#task_categories-graph-ml #arxiv-2007.08663 #region-us 
### Dataset Summary


The 'ethanol' dataset is a molecular dynamics (MD) dataset. The total energy and force labels for each dataset were computed using the PBE+vdW-TS electronic structure method. All geometries are in Angstrom, energies and forces are given in kcal/mol and kcal/mol/A respectively.### Supported Tasks and Leaderboards


'ethanol' should be used for organic molecular property prediction, a regression task on 1 property. The score used is Mean absolute errors (in meV) for energy prediction.


External Use
------------### PyGeometric


To load in PyGeometric, do the following:


Dataset Structure
-----------------### Data Properties### Data Fields


Each row of a given file is a graph, with:


* 'node\_feat' (list: #nodes x #node-features): nodes
* 'edge\_index' (list: 2 x #edges): pairs of nodes constituting edges
* 'edge\_attr' (list: #edges x #edge-features): for the aforementioned edges, contains their features
* 'y' (list: #labels): contains the number of labels available to predict
* 'num\_nodes' (int): number of nodes of the graph### Data Splits


This data is not split, and should be used with cross validation. It comes from the PyGeometric version of the dataset.


Additional Information
----------------------### Licensing Information


The dataset has been released under license unknown.


Please cite both papers when using these datasets in publications.