Learning mappings between equilibrium states of liquid systems using normalizing flows

  • 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.

Download full text files

Export metadata

Statistics

Number of document requests

Additional Services

Share in Twitter Search Google Scholar
Metadaten
Author:Alessandro Coretti, Sebastian FalknerORCiDGND, Phillip L. Geissler, Christoph Dellago
URN:urn:nbn:de:bvb:384-opus4-1219735
Frontdoor URLhttps://opus.bibliothek.uni-augsburg.de/opus4/121973
ISSN:0021-9606OPAC
ISSN:1089-7690OPAC
Parent Title (English):The Journal of Chemical Physics
Publisher:AIP Publishing
Type:Article
Language:English
Year of first Publication:2025
Publishing Institution:Universität Augsburg
Release Date:2025/05/15
Volume:162
Issue:18
First Page:184102
DOI:https://doi.org/10.1063/5.0253034
Institutes:Mathematisch-Naturwissenschaftlich-Technische Fakultät
Mathematisch-Naturwissenschaftlich-Technische Fakultät / Institut für Physik
Mathematisch-Naturwissenschaftlich-Technische Fakultät / Institut für Physik / AG Computergestützte Biologie
Dewey Decimal Classification:5 Naturwissenschaften und Mathematik / 53 Physik / 530 Physik
Licence (German):CC-BY 4.0: Creative Commons: Namensnennung (mit Print on Demand)