Molecular dynamics (MD) is a key tool for simulating the dynamical behavior of atomic systems. However, MD is inherently serial, which makes it difficult to increase single-system throughput with concurrent compute. To address this, we introduce Langevin Speculative Dynamics (LSD), a distributed and model-agnostic speculative sampler for accelerating MD without adding relative error. Inspired by speculative methods in language and diffusion modeling, LSD uses a draft model to propose fast simulation steps and verifies them in parallel with a slower target model, applying a transport map from the draft to the target distribution. We extend speculative sampling to second-order Langevin dynamics, derive the achievable speedup as a function of physical parameters, show that LSD generalizes across different systems and draft-target combinations with a 3-9x speedup, and confirm theoretically and empirically that LSD samples trajectories from its target model distribution.

翻译：分子动力学（MD）是模拟原子系统动力学行为的关键工具。然而，MD本质上具有串行性，这使得通过并行计算提升单系统通量变得困难。为解决这一问题，我们提出了朗之万推测动力学（LSD），这是一种分布式的、模型无关的推测采样器，可在不增加相对误差的情况下加速MD。受语言模型和扩散模型中推测方法的启发，LSD采用草稿模型快速提出模拟步长，并通过较慢的目标模型并行验证这些步长，同时利用输运映射将草稿分布修正至目标分布。我们将推测采样扩展至二阶朗之万动力学，推导了以物理参数表示的可行加速比，证明LSD在不同系统与草稿-目标组合中可实现3-9倍加速，并从理论与实验两方面证实LSD采样的轨迹严格遵循其目标模型分布。