graphs-datasets/MD17-uracil

Dataset Card for uracil

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:

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}
}