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Learning mappings between equilibrium states of liquid systems using normalizing flows.

Alessandro Coretti1, Sebastian Falkner1,2, Phillip L Geissler3

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Summary
This summary is machine-generated.

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.

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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.