Work

Learning mappings between equilibrium states of liquid systems using normalizing flows

Year: 2025

Type: article

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.

Source: The Journal of Chemical Physics

DOI: 10.1063/5.0253034

Authors: Alessandro Coretti, Sebastian Falkner, Phillip L. Geissler, Christoph Dellago

Institutions: University of Vienna, University of Augsburg, University of California, Berkeley

Language: English

Topic: Advanced Physical and Chemical Molecular Interactions

Subfield: Physical and Theoretical Chemistry

Field: Chemistry

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $3,500

Funder: Austrian Science Fund