Molecular Kinetics with Koopman Generators and Random Fourier Features

In this talk, I will present recent work on estimating kinetic properties of molecular systems - such as transition rates and correlation functions - using models for the Koopman generator. I will show that random Fourier features - a low-rank approximation technique for kernel methods - provide a versatile and efficient framework to estimate these models from data. I will present three use cases: first, a benchmark study on estimating slow transition timescales. Second, interpolation of kinetic properties across temperatures using generative models. Third, learning of coarse grained models which preserve transition timescales.