Super-resolution in Molecular Dynamics Trajectory Reconstruction with Bi-Directional Neural Networks

Understand

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.