{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,7]],"date-time":"2024-08-07T07:36:30Z","timestamp":1723016190644},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,8]]},"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.24963\/ijcai.2023\/836","type":"proceedings-article","created":{"date-parts":[[2023,8,11]],"date-time":"2023-08-11T08:31:30Z","timestamp":1691742690000},"page":"7145-7148","source":"Crossref","is-referenced-by-count":0,"title":["Optimized Crystallographic Graph Generation for Material Science"],"prefix":"10.24963","author":[{"given":"Astrid","family":"Klipfel","sequence":"first","affiliation":[{"name":"Centre de Recherche en Informatique de Lens (CRIL)"},{"name":"Unit\u00e9 de Catalyse et de Chimie du Solide (UCCS)"},{"name":"Laboratoire de Math\u00e9matiques de Lens (LML)"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ya\u00ebl","family":"Fr\u00e9gier","sequence":"additional","affiliation":[{"name":"Laboratoire de Math\u00e9matiques de Lens (LML)"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Adlane","family":"Sayede","sequence":"additional","affiliation":[{"name":"Unit\u00e9 de Catalyse et de Chimie du Solide (UCCS)"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zied","family":"Bouraoui","sequence":"additional","affiliation":[{"name":"Centre de Recherche en Informatique de Lens (CRIL)"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"10584","event":{"number":"32","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"acronym":"IJCAI-2023","name":"Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}","start":{"date-parts":[[2023,8,19]]},"theme":"Artificial Intelligence","location":"Macau, SAR China","end":{"date-parts":[[2023,8,25]]}},"container-title":["Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2023,8,11]],"date-time":"2023-08-11T08:56:55Z","timestamp":1691744215000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2023\/836"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2023,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2023\/836","relation":{},"subject":[],"published":{"date-parts":[[2023,8]]}}}