Optimized Crystallographic Graph Generation for Material Science
Astrid Klipfel Yaël Frégier Adlane Sayede Zied Bouraoui
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.