Abstract:

The rare-event sampling problem is one of the fundamental problems in statistical mechanics and particularly in molecular dynamics or Monte-Carlo simulations of molecules. Here I will introduce to Boltzmann-generating flows that combine invertible neural networks and statistical-mechanics based reweighting or resampling methods in order to train a machine-learning method to generate samples from the desired equilibrium distribution of the molecule or other many-body system. In particular, two recent developments will be described: equivariant flows that take symmetries in the molecular energy function into account, and Stochastic Normalizing Flows which combine deterministic invertible neural networks with stochastic sampling steps and are trained using path likelihood maximization techniques that have emerged in nonequilibrium statistical mechanics.

Biography:

Frank Noé has undergraduate degrees in electrical energineering and computer science and graduated in computer science and computational physics at University of Heidelberg. Frank is currently full professor for Mathematics, Computer Science and Physics at Freie Universität Berlin, Germany. Since 2015 he also holds an adjunct professorship in Chemistry at Rice University Houston, Texas.

Frank's research focuses on developing new Machine Learning methods for the physical sciences, especially molecular sciences. Frank received two awards of the European Research Council, an ERC starting grant in 2012 and an ERC consolidator grant in 2017. He received the early career award in theoretical Chemistry of the American Chemical Society in 2019 and he is ISI highly cited researcher since 2019.