{"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/crystal-graph-neural-networks-for-data-mining","title":"Crystal Graph Neural Networks for Data Mining in Materials Science","arxiv_id":null,"date":"2019-05-27","proceeding":"Technical report, RIMCS LLC 2019 5","authors":["Takenori Yamamoto"],"abstract":"Machine learning methods have been employed for materials prediction in various ways. It has recently been proposed that a crystalline material is represented by a multigraph called a crystal graph. Convolutional neural networks adapted to those graphs have successfully predicted bulk properties of materials with the use of equilibrium bond distances as spatial information. An investigation into graph neural networks for small molecules has recently shown that the no distance model performs almost as well as the distance model. This paper proposes crystal graph neural networks (CGNNs) that use no bond distances, and introduces a scale-invariant graph coordinator that makes up crystal graphs for the CGNN models to be trained on the dataset based on a theoretical materials database. The CGNN models predict the bulk properties such as formation energy, unit cell volume, band gap, and total magnetization for every testing material, and the average errors are less than the corresponding ones of the database. The predicted band gaps and total magnetizations are used for the metal-insulator and nonmagnet-magnet binary classifications, which result in success. This paper presents discussions about high- throughput screening of candidate materials with the use of the predicted formation energies, and also about the future progress of materials data mining on the basis of the CGNN architectures.","url_abs":"https://www.researchgate.net/publication/333667001_Crystal_Graph_Neural_Networks_for_Data_Mining_in_Materials_Science","url_pdf":"https://storage.googleapis.com/rimcs_cgnn/cgnn_matsci_May_27_2019.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":"crystal-graph-neural-networks-for-data-mining","repo_url":"https://github.com/Tony-Y/cgnn","is_official":1,"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-neural-network","task_name":"Graph Neural Network"},{"task_slug":"materials-screening","task_name":"Materials Screening"},{"task_slug":"total-magnetization","task_name":"Total Magnetization"}],"methods":[{"method_slug":"cgnn","method_name":"CGNN"},{"method_slug":"mpnn","method_name":"MPNN"}],"datasets_introduced":[{"slug":"oqmd-v1-2","name":"OQMD v1.2","full_name":"The Open Quantum Materials Database"}],"methods_introduced":[{"slug":"cgnn","name":"CGNN","full_name":"Crystal Graph Neural Network"}],"results":[{"leaderboard":"/sota/formation-energy-on-oqmd-v12","task":"Formation Energy","dataset":"OQMD v1.2","model":"CGNN Ensemble","rank_in_archive_order":1,"of":4,"metrics":{"MAE":"30.5"},"uses_additional_data":false},{"leaderboard":"/sota/formation-energy-on-oqmd-v12","task":"Formation Energy","dataset":"OQMD v1.2","model":"CGNN-192","rank_in_archive_order":2,"of":4,"metrics":{"MAE":"34.6"},"uses_additional_data":false},{"leaderboard":"/sota/formation-energy-on-oqmd-v12","task":"Formation Energy","dataset":"OQMD v1.2","model":"CGNN-160","rank_in_archive_order":3,"of":4,"metrics":{"MAE":"35.1"},"uses_additional_data":false},{"leaderboard":"/sota/formation-energy-on-oqmd-v12","task":"Formation Energy","dataset":"OQMD v1.2","model":"CGNN-128","rank_in_archive_order":4,"of":4,"metrics":{"MAE":"35.7"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}