{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/oqm9hk-a-large-scale-graph-dataset-for","title":"OQM9HK: A Large-Scale Graph Dataset for Machine Learning in Materials Science","arxiv_id":null,"date":"2022-09-30","proceeding":"Technical report, RIMCS LLC 2022 9","authors":["Takenori Yamamoto"],"abstract":"We introduce a large-scale dataset of quantum-mechanically calculated properties of crystalline materials for graph representation learning that contains approximately 900k entries (OQM9HK). This dataset is constructed on the basis of the Open Quantum Materials Database (OQMD) v1.5 containing more than one million entries, and is the successor to the OQMD v1.2 dataset containing approximately 600k entries (OQM6HK). We develop the graph creation algorithm to produce a binary edge-labeled (BEL) graph representing a crystalline material. The BEL graph has higher representability of crystal structure than the edge-unlabeled ones. In materials property prediction tasks, crystal graph neural networks trained on the BEL graph dataset perform better than ones on the other graph datasets. The OQM9HK graph dataset is available at the Zenodo repository, https://doi.org/10.5281/zenodo.7124330","url_abs":"https://www.researchgate.net/publication/364167371_OQM9HK_A_Large-Scale_Graph_Dataset_for_Machine_Learning_in_Materials_Science","url_pdf":"https://storage.googleapis.com/rimcs_cgnn/oqm9hk_dataset_Sep_30_2022.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"oqm9hk-a-large-scale-graph-dataset-for","repo_url":"https://github.com/Tony-Y/cgnn","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"band-gap","task_name":"Band Gap"},{"task_slug":"formation-energy","task_name":"Formation Energy"},{"task_slug":"graph-representation-learning","task_name":"Graph Representation Learning"},{"task_slug":"property-prediction","task_name":"Property Prediction"},{"task_slug":"representation-learning","task_name":"Representation Learning"},{"task_slug":"total-magnetization","task_name":"Total Magnetization"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/formation-energy-on-oqm9hk","task":"Formation Energy","dataset":"OQM9HK","model":"CGNN Full Ensemble","rank_in_archive_order":1,"of":4,"metrics":{"MAE":"0.03433"},"uses_additional_data":false},{"leaderboard":"/sota/formation-energy-on-oqm9hk","task":"Formation Energy","dataset":"OQM9HK","model":"CGNN Trio Ensemble","rank_in_archive_order":2,"of":4,"metrics":{"MAE":"0.03658"},"uses_additional_data":false},{"leaderboard":"/sota/formation-energy-on-oqm9hk","task":"Formation Energy","dataset":"OQM9HK","model":"CGNN","rank_in_archive_order":3,"of":4,"metrics":{"MAE":"0.04249 ± 0.00037"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}