发表机构
University of Bath; AITHYRA（巴斯大学; AITHYRA）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出Langevin流映射，扩展机器学习力场以学习随机积分器，实现大时间步分子动力学，速度提升一个数量级，并展示可迁移性。
AI 中文摘要
分子动力学模拟通过在许多飞秒级小时间步上积分Langevin方程来进行。这给估计在更长的时间尺度上发生的系综性质和转变动力学带来了挑战。我们引入了Langevin流映射，它将机器学习力场扩展到额外学习随机Langevin积分器。我们表明，Langevin流映射能够实现大时间步的分子动力学，并恢复系统准确的动力学性质，同时运行速度比当前的机器学习力场快一个数量级。此外，通过在多样化的分子数据集上训练，我们展示了迈向可迁移的Langevin流映射的路径。
英文摘要
Molecular dynamics simulations proceed by integrating the Langevin equations over many small femtosecond timesteps. This poses a challenge for estimating ensemble properties and transition dynamics that occur on much longer timescales. We introduce Langevin Flow Maps, which extend machine-learned force-fields to additionally learn the stochastic Langevin integrator. We show that Langevin Flow Maps enable large-timestep molecular dynamics and recover accurate dynamical properties of the system, while running an order of magnitude faster than current machine-learned force fields. Further, by training on a diverse molecular dataset, we demonstrate a path towards transferable Langevin Flow Maps.
