经典伊辛自旋态的可扩展多温度自由能采样
Scalable Multitemperature Free Energy Sampling of Classical Ising Spin States
AI总结：
针对生成模型在离散变量材料系统应用不足的问题，本文提出离散流匹配模型，可在宽温度范围高效采样自由能表面且扩展至更大晶格尺寸，在二维伊辛模型验证有效并开源代码。
摘要：
生成模型在采样具有连续变量的材料系统（如原子结构）方面取得了显著进展。然而，它们在离散变量（如原子类型或自旋态）上的应用仍未得到充分探索。在这项工作中，我们引入了一种离散流匹配模型，专为具有离散相空间坐标的系统（例如伊辛模型或晶格上的多组分系统）量身定制。该方法使单个模型能够在宽温度范围内采样自由能表面，且训练开销极小，模型生成可扩展到比训练集中更大的晶格尺寸。我们在二维伊辛模型上验证了该方法，展示了高效且可靠的自由能采样。这些结果突显了流匹配在离散系统中低成本、可扩展自由能采样的潜力，并表明其有望扩展到晶体材料中的炼金术自由度。本工作开发的代码库已公开，地址为https://github.com/tuoping/alchemicalFES。
英文摘要：
Generative models have advanced significantly in sampling material systems with continuous variables, such as atomistic structures. However, their application to discrete variables, like atom types or spin states, remains underexplored. In this work, we introduce a discrete flow matching model, tailored for systems with discrete phase-space coordinates (e.g., the Ising model or a multicomponent system on a lattice). This approach enables a single model to sample free energy surfaces over a wide temperature range with minimal training overhead, and the model generation is scalable to larger lattice sizes than those in the training set. We demonstrate our approach on the 2D Ising model, showing efficient and reliable free energy sampling. These results highlight the potential of flow matching for low-cost, scalable free energy sampling in discrete systems and suggest promising extensions to alchemical degrees of freedom in crystalline materials. The codebase developed for this work is openly available at https://github.com/tuoping/alchemicalFES.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
