Super-resolution in Molecular Dynamics Trajectory Reconstruction with Bi-Directional Neural Networks. (arXiv:2201.01195v1 [physics.comp-ph])

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 …

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