## Neural Networks from first-principles for rich datatypes

We design neural networks from first-princples to rigorously represent the rich datatypes present in the physical sciences. For example, we’ve created (3D) Euclidean neural networks and the accompanying software package `e3nn`

, to naturally handle as geometry and geometric tensors (scalars, vectors, matrices), which transform predicably under 3D rotations and translation; these are the datatypes of physical systems in 3D.

## Accelerating existing techniques and creating new capabilities for computational chemistry and material science

The “holy grail” of computational chemistry and materials discovery is to create an algorithm such that given a list of desirable properties, the algorithm would return an arrangement of atoms with those properties. We’re helping get us there by

- generating better starting points for expensive quantum mechanical calculations so they can converge faster,
- creating surrogate models to emulate these calculations altogether,
- designing algorithms to propose new hypothetical atomic structures that we can then study with existing methods, and
- aiding in the structural characterization of new materials.