Molecular dynamics simulations are a cornerstone in science, allowing to investigate from the system's thermodynamics to analyse intricate molecular interactions. In general, to create extended molecular trajectories can be a computationally expensive process, for example, when running $ab-initio$ simulations. Hence, repeating such calculations to either obtain more accurate thermodynamics or to get a higher resolution in the dynamics generated by a fine-grained quantum interaction can be time- and computationally-consuming. In this work, we explore different machine learning (ML) methodologies to increase the resolution of molecular dynamics trajectories on-demand within a post-processing step. As a proof of concept, we analyse the performance of bi-directional neural networks such as neural ODEs, Hamiltonian networks, recurrent neural networks and LSTMs, as well as the uni-directional variants as a reference, for molecular dynamics simulations (here: the MD17 dataset). We have found that Bi-LSTMs are the best performing models; by utilizing the local time-symmetry of thermostated trajectories they can even learn long-range correlations and display high robustness to noisy dynamics across molecular complexity. Our models can reach accuracies of up to 10$^{-4}$ angstroms in trajectory interpolation, while faithfully reconstructing several full cycles of unseen intricate high-frequency molecular vibrations, rendering the comparison between the learned and reference trajectories indistinguishable. The results reported in this work can serve (1) as a baseline for larger systems, as well as (2) for the construction of better MD integrators.
翻译:分子动态模拟是科学的基石, 使分子动态模拟成为了科学的基石, 从而可以从系统中的热力动力学中调查分析复杂的分子相互作用。 一般来说, 建立延伸分子分子轨迹可能是一个计算成本昂贵的过程, 比如运行 $ab- initio$ 的模拟。 因此, 重复这样的计算可以获取更准确的热力动力, 或者获得更准确的热力动力学, 或者是通过微微微分量互动产生的动力的更高分辨率, 可以是一个时间和计算耗时。 在这项工作中, 我们探索不同的机器学习( MLM ) 参考方法, 以增加分子动态轨迹在后处理步骤中按需解析的分子动态轨迹。 作为概念的证明,我们分析双向神经神经神经神经网络的性能, 例如神经内核、汉密尔顿网络、 恒定神经网络和LSTMMMM, 以及单向单向离子变异体作为参考, 用于分子动态模拟系统( 这里: MD17 数据集 ) 。 我们发现B- LSTM 是最佳的演化模型; 通过利用本地的內定數數數數的內基數數, 的內基數,, 以及我們內化的內部的內化的內化, 的內化的內化的內化, 的內化的內化的內化, 以及內化, 的內化的內化的內化, 的內化, 。