Learning mappings between equilibrium states of liquid systems using normalizing flows
- Author(s)
- Alessandro Coretti, Sebastian Falkner, Phillip L. Geissler, Christoph Dellago
- Abstract
Generative models and, in particular, normalizing flows are a promising tool in statistical mechanics to address the sampling problem in condensed-matter systems. In this work, we investigate the potential of normalizing flows to learn a transformation to map different liquid systems into each other while allowing at the same time to obtain an unbiased equilibrium distribution. We apply this methodology to the mapping of a small system of fully repulsive disks modeled via the Weeks-Chandler-Andersen potential into a Lennard-Jones system in the liquid phase at different coordinates in the phase diagram. We obtain an improvement in the relative effective sample size of the generated distribution up to a factor of six compared to direct reweighting. We show that this factor can have a strong dependency on the thermodynamic parameters of the source and target system.
- Organisation(s)
- Computational and Soft Matter Physics
- External organisation(s)
- Universität Augsburg, University of California, Berkeley
- Journal
- Journal of Chemical Physics
- Volume
- 162
- No. of pages
- 12
- ISSN
- 0021-9606
- DOI
- https://doi.org/10.48550/arXiv.2208.10420
- Publication date
- 05-2025
- Peer reviewed
- Yes
- Austrian Fields of Science 2012
- 103043 Computational physics, 103006 Chemical physics, 103029 Statistical physics
- ASJC Scopus subject areas
- General Physics and Astronomy, Physical and Theoretical Chemistry
- Portal url
- https://ucrisportal.univie.ac.at/en/publications/1273fb37-12e1-4b51-a8b2-5f44674cc968