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arXiv Machine Learning By Kyucheol Min, Elyssa Hofgard, Tess Smidt

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

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FrOGS is a hybrid discrete neural sampler that couples an autoregressive model with a continuous-time Markov chain, trained under a single shared loss to sample alloy configurations across many chemical conditions. It produces independent, unbiased configurations, estimates the partition function, and yields consistent thermodynamic observables on a common absolute free‑energy scale. The method matches exact results for the 2D Ising model and reproduces reference phase diagrams for AgPd and CuAu, avoiding mode collapse and correctly recovering the stability range of the CuAu$_3$ phase.

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