Grand Canonical Generators

Abstract:We introduce Grand Canonical Generators (GCG), a generative framework that extends Boltzmann generators to the grand canonical ensemble. We present two designs. The first conditions a variable-size generative model on the chemical potential, sampling particle number and configuration jointly. The second factorizes the grand canonical distribution into a particle-number distribution and the corresponding canonical Boltzmann density. This factorized formulation can use any existing Boltzmann generator for the canonical component, encodes the known linear chemical-potential dependence analytically, and yields a tractable likelihood that supports self-normalized importance sampling (SNIS). Empirically, GCG accurately reproduces grand canonical observables on a Lennard--Jones fluid and methane adsorption in a zeolite, demonstrating generalization across chemical potentials and correction via SNIS and grand canonical Monte Carlo.

Machine Learning (cs.LG); Machine Learning (stat.ML)

arXiv:2610.00683 [cs.LG]

(or arXiv:2610.00683v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2610.00683 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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