{"type":"ScholarlyArticle","title":"Optimized Crystallographic Graph Generation for Material Science","authors":[{"name":"Astrid Klipfel","slug":"astrid-klipfel","given":"Astrid","family":"Klipfel","affiliation":"Univ. Artois , UMR 8188 , Centre de Recherche en Informatique de Lens (CRIL) , F-62300 Lens , France.; Univ. Artois , UMR 8181 , Unité de Catalyse et de Chimie du Solide (UCCS) , F-62300 Lens , France.; Univ. Artois , UR 2462 , Laboratoire de Mathématiques de Lens (LML) , F-62300 Lens , France."},{"name":"Yaël Frégier","slug":"yael-fregier","given":"Yaël","family":"Frégier","affiliation":"Univ. Artois , UR 2462 , Laboratoire de Mathématiques de Lens (LML) , F-62300 Lens , France."},{"name":"Adlane Sayede","slug":"adlane-sayede","given":"Adlane","family":"Sayede","affiliation":"Univ. Artois , UMR 8181 , Unité de Catalyse et de Chimie du Solide (UCCS) , F-62300 Lens , France."},{"name":"Zied Bouraoui","slug":"zied-bouraoui","given":"Zied","family":"Bouraoui","affiliation":"Univ. Artois , UMR 8188 , Centre de Recherche en Informatique de Lens (CRIL) , F-62300 Lens , France."}],"abstract":"Graph neural networks are widely used in machine learning applied to chemistry, and in particular for material science discovery. For crystalline materials, however, generating graph-based representation from geometrical information for neural networks is not a trivial task. The periodicity of crystalline needs efficient implementations to be processed in real-time under a massively parallel environment. With the aim of training graph-based generative models of new material discovery, we propose an efficient tool to generate cutoff graphs and k-nearest-neighbours graphs of periodic structures within GPU optimization. We provide pyMatGraph a Pytorch-compatible framework to generate graphs in real-time during the training of neural network architecture. Our tool can update a graph of a structure, making generative models able to update the geometry and process the updated graph during the forward propagation on the GPU side. Our code is publicly available at https://github.com/aklipf/mat-graph.","doi":"10.24963/ijcai.2023/836","url":"https://ijcai.org/proceedings/2023/836","pdf":"https://ijcai.org/proceedings/2023/0836.pdf","markdown":"https://ijcai.org/proceedings/2023/836.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/836","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Demo Track","track":"Demo Track","number":836,"pages":{"first":7145,"last":7148},"keywords":["Multidisciplinary Topics and Applications: MDA: Energy, environment and sustainability","Multidisciplinary Topics and Applications: MDA: Life sciences","Multidisciplinary Topics and Applications: MDA: Physical sciences","Machine Learning: ML: Feature extraction, selection and dimensionality reduction"],"videos":[],"references":[{"position":1,"raw":"[Chen et al., 2019] Chi Chen, Weike Ye, Yunxing Zuo, Chen Zheng, and Shyue Ping Ong. 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