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README.md

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----
-licence: mit
----
-
-# Dataset Card for alchemy
-
-## 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](https://alchemy.tencent.com/)**
-- **Paper:**:  (see citation)
-- **Leaderboard:**: [Leaderboard](https://alchemy.tencent.com/)
-
-### Dataset Summary
-The `alchemy` dataset is a molecular dataset, called Alchemy, which lists 12 quantum mechanical properties of 130,000+ organic molecules comprising up to 12 heavy atoms (C, N, O, S, F and Cl), sampled from the GDBMedChem database.
-
- ### Supported Tasks and Leaderboards
-`alchemy` should be used for organic quantum molecular property prediction, a regression task on 12 properties. The score used is MAE.
-
-
-## 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 | 202578 |
-| average #nodes | 10.101387606810183 |
-| average #edges | 20.877326870011206 |
-
-### 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: 1 x #labels): contains the number of labels available to predict (here 1, equal to zero or one)
-- `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 mit.
-
-### 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{DBLP:journals/corr/abs-1906-09427,
-  author    = {Guangyong Chen and
-               Pengfei Chen and
-               Chang{-}Yu Hsieh and
-               Chee{-}Kong Lee and
-               Benben Liao and
-               Renjie Liao and
-               Weiwen Liu and
-               Jiezhong Qiu and
-               Qiming Sun and
-               Jie Tang and
-               Richard S. Zemel and
-               Shengyu Zhang},
-  title     = {Alchemy: {A} Quantum Chemistry Dataset for Benchmarking {AI} Models},
-  journal   = {CoRR},
-  volume    = {abs/1906.09427},
-  year      = {2019},
-  url       = {http://arxiv.org/abs/1906.09427},
-  eprinttype = {arXiv},
-  eprint    = {1906.09427},
-  timestamp = {Mon, 11 Nov 2019 12:55:11 +0100},
-  biburl    = {https://dblp.org/rec/journals/corr/abs-1906-09427.bib},
-  bibsource = {dblp computer science bibliography, https://dblp.org}
-}
-
-
-```

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