FrOGS：适用于不同化学条件下独立合金构型的离散神经采样器
FrOGS: Discrete Neural Sampler for Independent Alloy Configurations Across Chemical Conditions
- Massachusetts Institute of Technology（麻省理工学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
该研究提出FrOGS混合离散神经采样器，耦合自回归模型与CTMC，可在多化学条件下训练单个模型，在二维伊辛模型、AgPd和CuAu相图上表现良好，能恢复CuAu₃相稳定范围，优于基线SEGAL。
AI中文摘要：
预测合金的热力学性质需要在多种化学条件下对其构型进行采样，并在共同的绝对标度上恢复自由能。马尔可夫链蒙特卡洛（MCMC）是标准工具，但它需要在不同条件下进行单独模拟，还需使用热力学积分等辅助自由能方法将结果置于共同的绝对标度上。现代离散神经采样器通常以反向KL散度为目标，可能存在模式寻求或偏差问题。我们提出了自由能生成采样器（FrOGS），这是一种混合离散神经采样器，它将自回归模型与连续时间马尔可夫链（CTMC）耦合，在单个共享损失下联合训练。FrOGS生成独立同分布（i.i.d.）的构型，返回配分函数的无偏估计，并给出热力学可观测量的一致估计。我们在广泛的化学条件下训练单个模型，以在共同的绝对自由能标度上产生估计值。FrOGS在二维伊辛模型上匹配了精确的有限尺寸结果，在AgPd和CuAu的参考相图上也表现良好，且未出现模式崩溃。我们还与已发表的自回归基线SEGAL进行了比较，发现只有FrOGS能恢复CuAu₃相的稳定范围。
英文摘要：
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.
