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.

本文利用不同种类的机器学习模型，研究并提高了分子动力学模拟的分辨率，证实双向 LSTM 模型是表现最好的模型，其能够在轨迹插值方面达到 10^-4 埃的精度，从而为更大系统的建立和更好的分子动力学积分器的构建奠定了基础。