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README.md

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----
-licence: unknown
----
-
-# Dataset Card for salicylic_acid
-
-## 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 `salicylic_acid` 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
-`salicylic_acid` 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 | 220231 |
-| average #nodes | 16.0 |
-| average #edges | 208.2681717461586 |
-
-### 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}
-}
-
-
-```

dataset_infos.json

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