FrOGS: Discrete Neural Sampler for Independent Alloy Configurations Across Chemical Conditions

arXiv:2609.02948 · cond-mat.mtrl-sci, cond-mat.stat-mech, cs.LG · Submitted 2026-09-01 · Read on arXiv

cond-mat.mtrl-sci, cond-mat.stat-mech, cs.LG

Submitted: 2026-09-01

Updated: 2026-09-21

Comments: Submitted to the AI4Mat workshop at NeurIPS 2026

Project page: https://tminka.github.io/papers/message-passing/minka-divergence.pdf

License: http://creativecommons.org/licenses/by/4.0/

The gist: Predicting the thermodynamic properties of an alloy requires sampling its configurations across many chemical conditions and recovering free energies on a common absolute scale.

Terminology

Abstract

Predicting the thermodynamic properties of an alloy requires sampling its configurations across many chemical conditions and recovering free energies on a common absolute scale. Markov chain Monte Carlo (MCMC) is the standard tool, but it requires separate simulations at different conditions, and auxiliary free-energy methods such as thermodynamic integration are used to place results on a common absolute scale. Modern discrete neural samplers typically use reverse KL divergence as the objective and can be mode-seeking or biased. We present Free energy Offering Generative Sampler (FrOGS), a hybrid discrete neural sampler that couples an autoregressive model to a continuous-time Markov chain (CTMC) to be trained jointly under a single shared loss. FrOGS draws i.i.d. configurations, returns an unbiased estimate of the partition function, and gives consistent estimates of thermodynamic observables. We train a single model across a wide range of chemical conditions to produce estimates on a common absolute free-energy scale. FrOGS matches exact finite-size results on the 2D Ising model and reference phase diagrams for AgPd and CuAu, without mode collapse. We additionally compare to SEGAL, a published autoregressive baseline, and find that only FrOGS recovers the stability range of the CuAu 3 phase.

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