FrOGS: Discrete Neural Sampler for Independent Alloy Configurations Across Chemical Conditions
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 cha...
| Authors | Kyucheol Min, Elyssa Hofgard, Tess Smidt |
|---|---|
| Primary category | cond-mat.mtrl-sci |
| Published | 1 September 2026 |
| Last updated at source | 21 September 2026 |
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