**“**Euclidean Neural Networks* for Emulating Ab Initio Calculations and Generating Atomic Geometries**.”**

### Tuesday, December 3, from 4:30 to 5:30 p.m. — Physics/Astronomy Auditorium, room A118

Tess Smidt, Ph.D., Alvarez Postdoctoral Fellow in Computing Sciences at Lawrence Berkeley National Laboratory.

*[Watch a recording of this seminar on YouTube after it occurs.]*

### Abstract

Atomic systems (molecules, crystals, proteins, nanoclusters, etc.) are naturally represented by a set of coordinates in 3D space labeled by atom type. This is a challenging representation to use for neural networks because the coordinates are sensitive to 3D rotations and translations and there is no canonical orientation or position for these systems. We present a general neural network architecture that naturally handles 3D geometry and operates on the scalar, vector, and tensor fields that characterize physical systems. Our networks are locally equivariant to 3D rotations and translations at every layer. In this talk, we describe how the network achieves these equivariances and demonstrate the capabilities of our network using simple tasks. We’ll also present examples of applying Euclidean networks to applications in quantum chemistry and discuss techniques for using these networks to encode and decode geometry.

### Bio

Tess Smidt is the 2018 Alvarez Postdoctoral Fellow in Computing Sciences at Lawrence Berkeley National Laboratory. Her research interests include intelligent computational materials discovery and deep learning for atomic systems. She is currently designing algorithms that can propose new hypothetical atomic structures. Tess earned her PhD in physics from UC Berkeley in 2018 working with **Professor Jeffrey B. Neaton**. As a graduate student, she used quantum mechanical calculations to understand and systematically design the geometry and corresponding electronic properties of atomic systems. During her PhD, Tess spent a year as an intern on **Google’s Accelerated Science Team** where she developed a new type of convolutional neural network, called **Tensor Field Networks**, that can naturally handle 3D geometry and properties of physical systems. As an undergraduate at MIT, Tess engineered giant neutrino detectors in **Professor Janet Conrad’s group** and created a permanent science-art installation on MIT’s campus called the **Cosmic Ray Chandeliers**, which illuminate upon detecting cosmic-ray muons.

This event is open to the public.