University of Cambridge > Talks.cam > Lennard-Jones Centre > Optimizing sampling and free energy estimation with normalizing flows

Optimizing sampling and free energy estimation with normalizing flows

Add to your list(s) Download to your calendar using vCal

If you have a question about this talk, please contact Dr Christoph Schran.

Recently there has been a surge in using generative models, most notably normalizing flows, to address challenging problems in statistical mechanics either by developing new learned schemes or by addressing shortcomings of existing techniques. Efficient sampling from high-dimensional Boltzmann distributions given an energy function and accurate free energy estimation are two key problems in the field. In this talk we will focus on both topics and present our recent works on targeted free energy estimation, in which we combine free energy perturbation and Bennett’s acceptance ratio method with normalizing flows to obtain powerful estimators [1, 2]. We illustrate the potential of this approach by estimating solid free energies and briefly discuss the limitations of our current model.

[1] Wirnsberger, Ballard et al., Targeted free energy estimation via learned mappings, J. Chem. Phys. 153, 144112 (2020)

[2] Wirnsberger, Papamakarios, Ibarz et al., Normalizing flows for atomic solids, arXiv:2111.0869 (2021)

This talk is part of the Lennard-Jones Centre series.

Tell a friend about this talk:

This talk is included in these lists:

Note that ex-directory lists are not shown.

 

© 2006-2022 Talks.cam, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity