Graph Neural Networks

Graph neural networks (GNNs) have become popular tools for processing physics data. A GNN is a neural network that takes as input a graph object composed of nodes, edges, and global features and outputs another graph, which could be a single global feature in the case of binary classification. Many physics datasets can be most naturally represented as a graph or a point cloud and so GNNs may be the most effective deep learning tool to analyze them. These tools can encode the geometry of our complex data without requiring a regular grid and also respect other aspects of the data structure such as permutation invariance, symmetries, variable size, etc. These tools have a range of applicability including materials discovery, clustering, image segmentation, particle tracking, etc. The goal of this tutorial is to provide a hands-on introduction to GNNs for physicists by physicists.

Topics

• Basics of Graph Neural Networks

• Graph Neural Networks for Property Prediction, atomistic optimization and material discovery

• Graph Neural Networks for Materials