研究人员开发了一种名为可扩展推理时间退火（SITA）的新方法，可高效地对分子玻尔兹曼分布进行采样。SITA利用基于流的模型和基于能量的代理来估计重要性权重，从而避免了计算成本高昂的散度计算。该方法在丙氨酸二肽和三肽等分子系统上表现出最先进的性能，为复杂的模拟提供了一种更易于处理的解决方案。 AI
影响 引入了一种更高效的分子模拟方法，有望加速药物发现和材料科学研究。
排序理由 该集群包含两篇arXiv论文，详细介绍了用于分子分布采样的新计算方法。
AI 生成摘要 · Google Gemini · 来自 4 个来源。 我们如何撰写摘要 →
arXiv:2605.31498v1 Announce Type: new Abstract: A long standing challenge in computational chemistry and biophysics is efficiently sampling the Boltzmann distribution of molecules. Advances in generative modeling have been proposed to address the limitations of conventional sampl…
Scalable inference-time annealing method uses flow-based models with energy-based surrogates to efficiently sample Boltzmann distributions without costly divergence calculations.
A long standing challenge in computational chemistry and biophysics is efficiently sampling the Boltzmann distribution of molecules. Advances in generative modeling have been proposed to address the limitations of conventional sampling techniques by eliminating the computational …
arXiv:2606.02589v1 Announce Type: cross Abstract: Integrating over model uncertainty in factorial designs via Bayesian model averaging is hindered by the combinatorial explosion of interpretable interaction effects, often yielding a multimodal posterior, where standard Markov cha…