原子热力学景观的生成嵌套采样
Generative Nested Sampling of Atomistic Thermodynamic Landscapes
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中文总结 AI 辅助
该研究针对嵌套采样在原子系统应用中的瓶颈，提出 NS-Flows 方法，大幅减少能量评估与时间，揭示原子与引力波热力学景观的差异，为原子系统热力学模拟提供高效方案。
中文摘要 AI 辅助
嵌套采样（NS）可通过单次模拟解析原子系统的热力学，但其实际应用范围受限于每个似然约束系综内用于去相关采样 walker 的马尔可夫链更新。基于流的嵌套采样已消除了引力波（GW）推理的这一瓶颈，然而将其应用于原子系统并非简单的场景转换。通过对比类 GW150914 双黑洞似然与维度相当的八粒子二维 Lennard-Jones（LJ）系统，我们发现两类景观存在根本差异：原子系统的多模态是离散且组合性的，由粒子置换产生并被硬碰撞壁分隔，其坐标耦合是密集且集体性的；而引力波后验分布则呈现平滑的简并性与局域参数耦合。基于该分析，我们提出 NS-Flows：一种以 NS 能量边界为条件的条件正态化流，通过近期存活集合的滑动窗口进行训练，它取代了马尔可夫链蒙特卡洛（MCMC），采用经重要性加权拒绝重采样校正的直接并行采样。存活集合自洽地提供数据，使流训练无需结构化先验或预先存在的数据集。对于周期边界条件（PBC）下的 LJ 圆盘，该算法将能量评估减少两个数量级以上， wall-clock 时间缩短约三分之一，且当势能成本增加时，该优势会愈发显著。流的生成效率还可作为物理诊断指标：其沿退火轨迹非单调变化，在密集无序区域最低，且可通过约束系综的内部模式复杂性及训练窗口内的目标漂移定量描述，这表明当前流架构的难点是类液体系综，而非先验-目标分离。
英文摘要
Nested sampling (NS) resolves the thermodynamics of an atomistic system from a single simulation, but its practical reach is limited by the Markov-chain updates needed to decorrelate walkers within each likelihood-constrained ensemble. Flow-based NS has reduced this bottleneck for gravitational-wave (GW) inference, yet its transfer to atomistic systems is not merely a change of application. Comparing a GW150914-like binary-black-hole likelihood with an eight-particle two-dimensional Lennard-Jones (LJ) system of comparable dimensionality, we show that the two landscapes differ fundamentally: atomistic multimodality is discrete and combinatorial, generated by particle permutations separated by hard collision walls, and its coordinate coupling is dense and collective, whereas the GW posterior exhibits smooth degeneracies and localized parameter coupling. Guided by this diagnosis, we introduce NS-Flows: a single conditional normalizing flow, conditioned on the NS energy bound and trained on a sliding window of recent live sets, that replaces MCMC by direct parallel draws corrected by importance-weighted rejection resampling. Live sets supply the data self-consistently, allowing flow training without structured priors or a pre-existing dataset. For LJ disks under periodic boundary conditions, the algorithm can reduce energy evaluations by two orders of magnitude and wall-clock time by about 30%, an advantage that becomes more favorable as the cost of the potential grows. The flow's generative efficiency further acts as a physical diagnostic: it varies non-monotonically along the annealing trajectory, is lowest in the dense disordered regime, and is quantitatively captured by the constrained ensemble's internal mode complexity together with target drift across the training window, identifying liquid-like ensembles, not prior-target separation, as the hard case for current flow architectures.
发表机构
- University of Vienna（维也纳大学）
- TU Wien（维也纳技术大学）
- University of Augsburg（奥格斯堡大学）
机构由 AI 辅助整理，请以论文原文为准。
