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README.md
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---
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licence: unknown
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---
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# Dataset Card for naphthalene
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## Table of Contents
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- [Table of Contents](#table-of-contents)
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- [Dataset Description](#dataset-description)
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- [Dataset Summary](#dataset-summary)
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- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
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- [External Use](#external-use)
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- [PyGeometric](#pygeometric)
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- [Dataset Structure](#dataset-structure)
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- [Data Properties](#data-properties)
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- [Data Fields](#data-fields)
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- [Data Splits](#data-splits)
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- [Additional Information](#additional-information)
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- [Licensing Information](#licensing-information)
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- [Citation Information](#citation-information)
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- [Contributions](#contributions)
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## Dataset Description
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- **[Homepage](http://www.sgdml.org/#datasets)**
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- **Paper:**: (see citation)
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### Dataset Summary
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The `naphthalene` 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.
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### Supported Tasks and Leaderboards
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`naphthalene` 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.
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## External Use
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### PyGeometric
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To load in PyGeometric, do the following:
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```python
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from datasets import load_dataset
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from torch_geometric.data import Data
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from torch_geometric.loader import DataLoader
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dataset_hf = load_dataset("graphs-datasets/<mydataset>")
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# For the train set (replace by valid or test as needed)
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dataset_pg_list = [Data(graph) for graph in dataset_hf["train"]]
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dataset_pg = DataLoader(dataset_pg_list)
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```
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## Dataset Structure
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### Data Properties
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| property | value |
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|---|---|
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| scale | big |
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| #graphs | 226255 |
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| average #nodes | 18.0 |
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| average #edges | 254.73246234354005 |
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### Data Fields
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Each row of a given file is a graph, with:
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- `node_feat` (list: #nodes x #node-features): nodes
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- `edge_index` (list: 2 x #edges): pairs of nodes constituting edges
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- `edge_attr` (list: #edges x #edge-features): for the aforementioned edges, contains their features
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- `y` (list: #labels): contains the number of labels available to predict
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- `num_nodes` (int): number of nodes of the graph
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### Data Splits
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This data is not split, and should be used with cross validation. It comes from the PyGeometric version of the dataset.
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## Additional Information
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### Licensing Information
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The dataset has been released under license unknown.
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### Citation Information
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```
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@inproceedings{Morris+2020,
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title={TUDataset: A collection of benchmark datasets for learning with graphs},
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author={Christopher Morris and Nils M. Kriege and Franka Bause and Kristian Kersting and Petra Mutzel and Marion Neumann},
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booktitle={ICML 2020 Workshop on Graph Representation Learning and Beyond (GRL+ 2020)},
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archivePrefix={arXiv},
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eprint={2007.08663},
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url={www.graphlearning.io},
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year={2020}
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}
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```
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```
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@article{Chmiela_2017,
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doi = {10.1126/sciadv.1603015},
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url = {https://doi.org/10.1126%2Fsciadv.1603015},
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year = 2017,
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month = {may},
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publisher = {American Association for the Advancement of Science ({AAAS})},
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volume = {3},
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number = {5},
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author = {Stefan Chmiela and Alexandre Tkatchenko and Huziel E. Sauceda and Igor Poltavsky and Kristof T. Schütt and Klaus-Robert Müller},
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title = {Machine learning of accurate energy-conserving molecular force fields},
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journal = {Science Advances}
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}
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```
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