Nested sampling from equivariant Graph Neural Networks

The main research focus is to understand precisely why certain methods, such as neural networks, are suited to the task of predicting data in the fields of materials science and molecular modelling, how architectural changes influence this ability and how this understanding can be translated into even better algorithms.

This entails the exploration of the parameter space of the neural networks.

As part of this, sampling algorithms will be advanced which explore the configuration space of molecules and materials, enabling property prediction from first principles.