We consider parallel trajectory splicing (ParSplice), a specialized molecular dynamics method that extends simulation timescales through a parallel-in-time strategy, enabling parallel speedups proportional to the number of worker-processes deployed. In practice, the ability for ParSplice to scale significantly improves when it is possible to predict the future evolution of the atomistic trajectory. We propose improved predictive statistical models that are built on-the-fly in order to maximize computational efficiency. By imposing physical constraints and explicitly considering uncertainties in model estimation we show a significant improvement in the scalability of ParSplice, and hence a corresponding increase in the timescales that can be reached by direct simulation.

中文翻译：

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利用平行轨迹拼接利用模型不确定性来改善长时间仿真的可扩展性

我们考虑了并行轨迹拼接（ParSplice），这是一种特殊的分子动力学方法，通过并行时间策略扩展了仿真时标，从而使并行加速与所部署的工作进程数量成正比。实际上，当可以预测原子轨迹的未来演变时，ParSplice缩放的能力会大大提高。我们提出了即时构建的改进的预测统计模型，以最大程度地提高计算效率。通过施加物理约束并明确考虑模型估计中的不确定性，我们显示了ParSplice的可伸缩性有了显着改善，因此，直接仿真可以达到的时标也相应增加。