Deep learning for molecular physics

There has been a surge of interest in machine learning in the past few years, and deep learning techniques are more and more integrated into the way we do quantitative science. A particularly exciting case for deep learning is molecular physics, where some of the "superpowers" of machine learning can make a real difference in addressing hard and fundamental computational problems - on the other hand the rigorous physical footing of these problems guides us in how to pose the learning  problem and making the design decisions for the learning architecture.  In this lecture I will review some of our recent contributions in marrying deep learning with statistical mechanics, rare-event sampling and quantum mechanics.