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--- |
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licence: unknown |
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task_categories: |
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- graph-ml |
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--- |
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# Dataset Card for uracil |
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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 `uracil` 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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`uracil` 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 | 133769 | |
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| average #nodes | 12.0 | |
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| average #edges | 128.88676085818943 | |
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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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``` |
