Orateur
Max Welling
(U. Amsterdam / MSR)
Description
Graph Neural Networks (GNNs) have proven to be a versatile tool to predict properties of molecules, generative molecules and even predict solutions of a partial differential equation (PDE). Many physical application domains also exhibit symmetries which can be incorporated into the GNNs through equivariant convolutions or data augmentation. In this talk I will explain how this tool can be leveraged to generate molecules from their equilibrium distribution, possibly conditioned on some properties, and even predict solutions of a PDE.
