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.
Author: | Alessandro Coretti, Sebastian FalknerORCiDGND, Phillip L. Geissler, Christoph Dellago |
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URN: | urn:nbn:de:bvb:384-opus4-1219735 |
Frontdoor URL | https://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): | ![]() |