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Learning mappings between equilibrium states of liquid systems using normalizing flows
Alessandro Coretti1, Sebastian Falkner1,2, Phillip L Geissler3
1Faculty of Physics, University of Vienna, 1090 Vienna, Austria.
The Journal of Chemical Physics
|May 8, 2025
Summary
Normalizing flows improve sampling in condensed-matter systems by mapping liquid states. This method enhances effective sample size up to sixfold compared to direct reweighting, depending on thermodynamic parameters.
Area of Science:
- Statistical Mechanics
- Condensed-Matter Physics
- Computational Physics
Background:
- Generative models, specifically normalizing flows, offer solutions for sampling challenges in complex physical systems.
- Efficient sampling is crucial for understanding equilibrium properties of condensed-matter systems.
Purpose of the Study:
- To explore the efficacy of normalizing flows in transforming between different liquid systems.
- To achieve unbiased equilibrium distributions for target systems using learned transformations.
- To assess the performance improvement over traditional reweighting techniques.
Main Methods:
- Application of normalizing flows to map a Weeks-Chandler-Andersen (WCA) potential system (fully repulsive disks) to a Lennard-Jones (LJ) system.
- Simulation of liquid phases at various thermodynamic conditions.
- Quantification of sampling efficiency using relative effective sample size.
Main Results:
- Normalizing flows successfully learned transformations between WCA and LJ liquid systems.
- Significant improvements in relative effective sample size, up to a factor of six, were observed compared to direct reweighting.
- The performance gain demonstrated a strong dependence on the thermodynamic parameters of both source and target systems.
Conclusions:
- Normalizing flows are a powerful tool for enhancing sampling efficiency in condensed-matter simulations.
- The learned mappings enable unbiased equilibrium distribution generation for target systems.
- Thermodynamic conditions critically influence the effectiveness of normalizing flow-based transformations.
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