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.
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| Author: | Alessandro Coretti, Sebastian FalknerORCiDGND, Phillip L. Geissler, Christoph Dellago |
|---|---|
| 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): |  CC-BY 4.0: Creative Commons: Namensnennung (mit Print on Demand) |
