Timewarp：通过学习时间粗粒化动力学实现可迁移的分子动力学加速
Timewarp: Transferable Acceleration of Molecular Dynamics by Learning Time-Coarsened Dynamics
AI总结：
Timewarp 利用在 MD 轨迹上离线训练的归一化流作为 MCMC 提议分布，学习跨越大时间步的粗粒化动力学，实现对不同小肽系统可迁移的全原子分子动力学采样加速。
摘要：
分子动力学（MD）模拟是一种广泛使用的分子系统模拟技术，最常见的是在全原子分辨率下进行，其运动方程以飞秒量级（$1\ extrm{fs}=10^{-15}\ extrm{s}$）的时间步长进行积分。MD 常用于计算平衡性质，这需要从平衡分布（如玻尔兹曼分布）中采样。然而，许多重要过程（如结合和折叠）发生在毫秒或更长的时间尺度上，无法用传统 MD 高效采样。此外，对于每个所研究的分子系统，都需要重新执行 MD 模拟。我们提出 Timewarp，一种增强采样方法，该方法在针对玻尔兹曼分布的马尔可夫链蒙特卡洛方法中使用归一化流作为提议分布。该流在 MD 轨迹上进行离线训练，学习在时间上迈出大步，模拟 $10^{5} - 10^{6}\:\ extrm{fs}$ 的分子动力学。关键的是，Timewarp 可在分子系统之间迁移：一旦训练完成，我们证明它能泛化到未见过的全原子分辨率小肽（2-4 个氨基酸），探索它们的亚稳态，并相比标准 MD 提供壁钟时间上的采样加速。我们的方法构成了迈向通用、可迁移的 MD 加速算法的重要一步。
英文摘要：
Molecular dynamics (MD) simulation is a widely used technique to simulate molecular systems, most commonly at the all-atom resolution where equations of motion are integrated with timesteps on the order of femtoseconds ($1\textrm{fs}=10^{-15}\textrm{s}$). MD is often used to compute equilibrium properties, which requires sampling from an equilibrium distribution such as the Boltzmann distribution. However, many important processes, such as binding and folding, occur over timescales of milliseconds or beyond, and cannot be efficiently sampled with conventional MD. Furthermore, new MD simulations need to be performed for each molecular system studied. We present Timewarp, an enhanced sampling method which uses a normalising flow as a proposal distribution in a Markov chain Monte Carlo method targeting the Boltzmann distribution. The flow is trained offline on MD trajectories and learns to make large steps in time, simulating the molecular dynamics of $10^{5} - 10^{6}\:\textrm{fs}$. Crucially, Timewarp is transferable between molecular systems: once trained, we show that it generalises to unseen small peptides (2-4 amino acids) at all-atom resolution, exploring their metastable states and providing wall-clock acceleration of sampling compared to standard MD. Our method constitutes an important step towards general, transferable algorithms for accelerating MD.
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