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

• URL: http://arxiv.org/abs/2609.02948v1
• Date: Tue, 01 Sep 2026 21:38:36 GMT
• Title: FrOGS: Discrete Neural Sampler for Independent Alloy Configurations Across Chemical Conditions
• Abstract summary: Free energy Offering Generative Sampler (FrOGS) is a hybrid discrete neural sampler that couples an autoregressive model to a continuous-time Markov chain.<n>We train a single model across a wide range of chemical conditions to produce estimates on a common absolute free-energy scale.
• Score: 0.11122493020689471
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• A Generative Sampler for distributions with possible discrete parameter based on Reversibility [9.349846971147256]
  We propose a unified, target-gradient-free generative sampling framework applicable across diverse state spaces.<n>We minimize the Maximum Mean Discrepancy (MMD) between the joint distributions of forward and backward Markov trajectories.<n> Experiments show that our framework accurately reproduces thermodynamic observables and captures mode-switching behavior across all regimes.
  arXiv  Detail & Related papers  (2026-03-10T06:32:51Z)
• SYK thermal expectations are classically easy at any temperature [49.788604174558564]
  We give a simple classical algorithm that approximates thermal expectations.<n>We show it has quasi-polynomial cost $nO(log n/)$ for all temperatures above a phase transition in the free energy.
  arXiv  Detail & Related papers  (2026-02-26T04:48:32Z)
• Impossibility of Refrigeration and Engine Operation in Minimal Qubit Repeated-Interaction Models [39.146761527401424]
  We investigate the operation of a qubit as a quantum thermal device within the repeated interaction framework.<n>We analyze two minimal models: an alternating-coupling setup, and a simultaneous-coupling setup.<n>Our results establish fundamental limitations on qubit-based quantum thermal machines operating under Markovian repeated interactions.
  arXiv  Detail & Related papers  (2026-02-20T15:53:52Z)
• Thermal-Drift Sampling: Generating Random Thermal Ensembles for Quantum Chaos Diagnostics [7.678849469440775]
  Random thermal states of many-body Hamiltonians underpin studies of thermalization, chaos, and quantum phase transitions.<n>We introduce the thermal-drift channel, a measurement-based operation that implements a tunable nonunitary drift along a chosen Pauli term.<n>We present a measurement-controlled sampling algorithm that generates thermal states together with their Hamiltonian "labels" for general physical models.
  arXiv  Detail & Related papers  (2026-02-05T17:27:40Z)
• Equilibrium Matching: Generative Modeling with Implicit Energy-Based Models [52.74448905289362]
  EqM is a generative modeling framework built from an equilibrium dynamics perspective.<n>By replacing time-conditional velocities with a unified equilibrium landscape, EqM offers a tighter bridge between flow and energy-based models.
  arXiv  Detail & Related papers  (2025-10-02T17:59:06Z)
• Progressive Inference-Time Annealing of Diffusion Models for Sampling from Boltzmann Densities [93.13866975467549]
  We propose Progressive Inference-Time Annealing (PITA) to learn diffusion-based samplers.<n>PITA combines two complementary techniques: Annealing of the Boltzmann distribution and Diffusion smoothing.<n>It enables equilibrium sampling of N-body particle systems, Alanine Dipeptide, and tripeptides in Cartesian coordinates.
  arXiv  Detail & Related papers  (2025-06-19T17:14:22Z)
• Energy-Based Coarse-Graining in Molecular Dynamics: A Flow-Based Framework without Data [0.0]
  Coarse-grained (CG) models provide an effective route to reducing the complexity of molecular simulations.<n>We introduce a fully data-free, generative framework for CG that directly targets the all-atom Boltzmann distribution.<n>We show that the method captures all relevant modes of the Boltzmann distribution, reconstructs atomic configurations, and automatically learns physically meaningful CG representations.
  arXiv  Detail & Related papers  (2025-04-29T17:05:27Z)
• Iterated Denoising Energy Matching for Sampling from Boltzmann Densities [109.23137009609519]
  Iterated Denoising Energy Matching (iDEM) iDEM alternates between (I) sampling regions of high model density from a diffusion-based sampler and (II) using these samples in our matching objective. We show that the proposed approach achieves state-of-the-art performance on all metrics and trains $2-5times$ faster.
  arXiv  Detail & Related papers  (2024-02-09T01:11:23Z)
• Deterministic Gibbs Sampling via Ordinary Differential Equations [77.42706423573573]
  This paper presents a general construction of deterministic measure-preserving dynamics using autonomous ODEs and tools from differential geometry. We show how Hybrid Monte Carlo and other deterministic samplers follow as special cases of our theory.
  arXiv  Detail & Related papers  (2021-06-18T15:36:09Z)
• Robust Finite Mixture Regression for Heterogeneous Targets [70.19798470463378]
  We propose an FMR model that finds sample clusters and jointly models multiple incomplete mixed-type targets simultaneously. We provide non-asymptotic oracle performance bounds for our model under a high-dimensional learning framework. The results show that our model can achieve state-of-the-art performance.
  arXiv  Detail & Related papers  (2020-10-12T03:27:07Z)
• Towards quantum simulation of Sachdev-Ye-Kitaev model [5.931069258860319]
  We study a simplified version of the Sachdev-Ye-Kitaev (SYK) model with real interactions by exact diagonalization. A quantum phase transition from a chaotic state to an integrable state is observed by increasing the discrete separation.
  arXiv  Detail & Related papers  (2020-03-03T14:18:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.