Temperature Steerable Flows and Boltzmann Generators

TL;DR

This paper introduces temperature-steerable flows (TSF), a novel method that generates a family of probability densities across different temperatures, enabling efficient sampling of physical systems in various thermodynamic states.

Contribution

The paper proposes TSF, a new type of flow model that can generate distributions at different temperatures, extending Boltzmann generators for generalized ensemble sampling.

Findings

TSF can generate distributions at multiple temperatures.

TSF enables sampling across thermodynamic states.

Improves efficiency of physical system simulations.

Abstract

Boltzmann generators approach the sampling problem in many-body physics by combining a normalizing flow and a statistical reweighting method to generate samples in thermodynamic equilibrium. The equilibrium distribution is usually defined by an energy function and a thermodynamic state. Here we propose temperature-steerable flows (TSF) which are able to generate a family of probability densities parametrized by a choosable temperature parameter. TSFs can be embedded in generalized ensemble sampling frameworks to sample a physical system across multiple thermodynamic states.