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.
Date:
12 March 2021, 14:00 (Friday, 8th week, Hilary 2021)
Venue:
Mathematical Institute, Woodstock Road OX2 6GG
Speaker:
Dr Frank Noe (Freie Universitat, Berlin)
Organising department:
Mathematical Institute
Organiser:
Sara Jolliffe (University of Oxford)
Organiser contact email address:
sara.jolliffe@maths.ox.ac.uk
Host:
Dr Peter Minary (University of Oxford)
Part of:
Mathematical Biology and Ecology
Booking required?:
Not required
Audience:
Members of the University only
Editor:
Sara Jolliffe
