Create README.md

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}
+}
+
+
+```