تقرير
Optimized Crystallographic Graph Generation for Material Science
العنوان: | Optimized Crystallographic Graph Generation for Material Science |
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المؤلفون: | Klipfel, Astrid, Frégier, Yaël, Sayede, Adlane, Bouraoui, Zied |
سنة النشر: | 2023 |
المجموعة: | Computer Science Condensed Matter |
مصطلحات موضوعية: | Condensed Matter - Materials Science, Computer Science - Machine Learning |
الوصف: | 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. |
نوع الوثيقة: | Working Paper |
URL الوصول: | http://arxiv.org/abs/2307.05380 |
رقم الأكسشن: | edsarx.2307.05380 |
قاعدة البيانات: | arXiv |
الوصف غير متاح. |