English(EN) Learning Hamiltonian Flow Maps: Mean Flow Consistency for Large-Timestep Molecular Dynamics

新方法实现大时间步分子动力学模拟

作者 PulseAugur 编辑部 · [1 个来源] · 2026-07-01 04:00

研究人员开发了一个新的模拟哈密顿系统的框架，可以显著提高分子动力学模拟的效率。这种称为平均流一致性的方法通过预测选定周期内相空间的平均演化，允许在模拟中使用更大的时间步长。与以前的技术不同，它不需要访问未来状态，使其更具成本效益，并且可以应用于机器学习力场（MLFF），而无需昂贵的轨迹生成。 AI

影响实现更高效、更具成本效益的分子动力学模拟，可能加速药物发现和材料科学研究。

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arXiv cs.LG TIER_1 English(EN) · Winfried Ripken, Michael Plainer, Gregor Lied, Thorben Frank, Oliver T. Unke, Stefan Chmiela, Frank No\'e, Klaus-Robert M\"uller · 2026-07-01 04:00

学习哈密顿流图：大时间步长分子动力学的平均流一致性

arXiv:2601.22123v4 Announce Type: replace Abstract: Simulating the long-time evolution of Hamiltonian systems is limited by the small timesteps required for stable numerical integration. To overcome this constraint, we introduce a framework to learn Hamiltonian Flow Maps by predi…