OQM9HK: A Large-scale Graph Dataset for Machine Learning in Materials Science

This is a large-scale graph dataset of materials science based on the Open Quantum Materials Database (OQMD) v1.5 .

Technical Report

RIMCS Website

Data Loading

A Python code example:

import sys
sys.path.append('/your/path/to/data/OQM9HK_BEL')
import OQM9HK

bel_path='/your/path/to/data/OQM9HK_BEL'

config = OQM9HK.load_config(path=bel_path)
print(config['atomic_numbers'])

split = OQM9HK.load_split(path=bel_path)
print(len(split['train']), len(split['val']), len(split['test']))

graph_data = OQM9HK.load_graph_data(path=bel_path)
name = next(iter(graph_data)) # Frist entry's name
graph = graph_data[name] # Graph object
print(graph.nodes)
print(graph.edge_sources)
print(graph.edge_targets)

dataset = OQM9HK.load_targets(path=bel_path) # Pandas dataframe
print(dataset)
train_set = dataset.iloc[split['train']]
val_set = dataset.iloc[split['val']]
test_set = dataset.iloc[split['test']]