用平均流一致性学习哈密顿流映射，实现大步长分子动力学稳定模拟。
Learning Hamiltonian Flow Maps: Mean Flow Consistency for Large-Timestep Molecular Dynamics
- 通过预测相空间平均演化，实现大时间步更新
- 在多种哈密顿系统上验证，支持远超传统积分器的步长
- 可直接训练于无轨迹的机器学习力场数据，适合分子模拟场景
模拟哈密顿系统的长时间演化受限于数值积分所需的微小时间步长。为突破此限制，我们提出一种学习哈密顿流映射的框架，通过预测选定时间跨度内的相空间平均演化，实现远超经典积分器稳定极限的大时间步更新。为此，我们引入了时间平均哈密顿动力学的平均流一致性条件。与以往方法不同，该方法可在独立相空间样本上训练，无需未来状态信息，避免了昂贵的轨迹生成过程。在多种哈密顿系统上验证有效，尤其提升了基于机器学习力场（MLFF）的分子动力学模拟性能。模型训练与推理成本相当，但支持显著更大的积分步长，且可直接在广泛可用的无轨迹MLFF数据集上训练。
原文摘要 · Abstract (English)
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 predicting the mean phase-space evolution over a chosen time span, enabling stable large-timestep updates far beyond the stability limits of classical integrators. To this end, we impose a Mean Flow consistency condition for time-averaged Hamiltonian dynamics. Unlike prior approaches, this allows training on independent phase-space samples without access to future states, avoiding expensive trajectory generation. Validated across diverse Hamiltonian systems, our method in particular improves upon molecular dynamics simulations using machine-learned force fields (MLFF). Our models maintain comparable training and inference cost, but support significantly larger integration timesteps while trained directly on widely-available trajectory-free MLFF datasets.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。