Modern Molecular NNs

11. Modern Molecular NNsΒΆ

We have seen two chapters about equivariances in Input Data & Equivariances and Equivariant Neural Networks. We have seen one chapter on dealing with molecules as objects with permutation equivariance Graph Neural Networks. We will combine these ideas and create neural networks that can treat arbitrary molecules with point clouds and permutation equivariance. We already saw SchNet is able to do this by working with an invariant point cloud representation (distance to atoms), but modern networks mix in ideas from Equivariant Neural Networks. This is a highly-active research area, especially for predicting energies, forces, and relaxed structures of molecules.

Audience & Objectives

This chapter assumes you have read Input Data & Equivariances, Equivariant Neural Networks, and Graph Neural Networks. You should be able to

  • Categorize a task (features/labels) by equivariance

  • Understand body-ordered expansions

  • Differentiate models based on their message passing, message type, and body-ordering


This chapter is in progress