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English(EN) Generative Nested Sampling of Atomistic Thermodynamic Landscapes

新的NS-Flows方法可大幅缩短原子模拟时间

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种名为NS-Flows的新方法，可显著加快确定原子系统热力学性质的过程。该方法采用了之前用于引力波推断的流基技术，以解决传统嵌套采样算法中的计算瓶颈。通过用条件归一化流替换马尔可夫链更新，NS-Flows可将Lennard-Jones圆盘等系统的能量评估次数减少两个数量级以上，并将实际运行时间减少约三分之一。 AI

影响 该方法可以通过加速热力学景观的模拟来加速材料科学研究。

排序理由 在科学论文中发布新的计算方法。[lever_c_demoted from research: ic=1 ai=0.7]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新的NS-Flows方法可大幅缩短原子模拟时间

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在科学论文中发布新的计算方法。[lever_c_demoted from research: ic=1 ai=0.7]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Alessandro Coretti, Nico Unglert, Sebastian Falkner, Georg K. H. Madsen, Christoph Dellago ·

    原子热力学景观的生成式嵌套采样

    arXiv:2609.03193v1 Announce Type: cross Abstract: 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 ense…

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