Super-resolution in Molecular Dynamics Trajectory Reconstruction with Bi-Directional Neural Networks
Abstract
Molecular dynamics (MD) simulations are a cornerstone in science, enabling the investigation of a
system’s thermodynamics all the way to analyzing intricate molecular interactions. In general,
creating 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 computational resource-consuming. In this work, we
explore different machine learning methodologies to increase the resolution of MD trajectories
on-demand within a post-processing step. As a proof of concept, we analyse the performance of
bi-directional neural networks (NNs) such as neural ODEs, Hamiltonian networks, recurrent NNs
and long short-term memories, as well as the uni-directional variants as a reference, for MD
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 Å in trajectory interpolation, which
leads to the faithful reconstruction of several unseen high-frequency molecular vibration cycles.
This renders 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.
system’s thermodynamics all the way to analyzing intricate molecular interactions. In general,
creating 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 computational resource-consuming. In this work, we
explore different machine learning methodologies to increase the resolution of MD trajectories
on-demand within a post-processing step. As a proof of concept, we analyse the performance of
bi-directional neural networks (NNs) such as neural ODEs, Hamiltonian networks, recurrent NNs
and long short-term memories, as well as the uni-directional variants as a reference, for MD
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 Å in trajectory interpolation, which
leads to the faithful reconstruction of several unseen high-frequency molecular vibration cycles.
This renders 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.
Research Areas
