Create README.md

README.md

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+---
+licence: unknown
+---
+
+# Dataset Card for uracil
+
+## 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 `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.
+
+
+### Supported Tasks and Leaderboards
+`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.
+
+
+## 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 | 133769 |
+| average #nodes | 12.0 |
+| average #edges | 128.88676085818943 |
+
+### 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
+```
+@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}
+}
+
+
+```