Abstract

See the TCM graduate teaching page for further information.

In this series of graduate lectures we will study the application of deep neural networks to the approximation of wavefunctions. Since 2017 there has been a surge of interest in this area and this looks set to accelerate. Knowledge of quantum mechanics will be assumed up to early graduate level but familiarity with deep neural networks is not essential.

In the first part of this lecture we will discover self-attention which is a crucial component in many modern advances for AI in general. We will then discuss the use of self-attention for many electron systems. In the second part of the lecture we will put what we have learned in context of the broader field of computational quantum mechanics and reflect on open questions and potential extensions.