Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Super-resolution in Molecular Dynamics Trajectory Reconstruction with
  Bi-Directional Neural Networks   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.

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The categories of this paper are: dg-ga
4
The categories of this paper are: cond-mat
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The categories of this paper are: patt-sol
0
The categories of this paper are: hep-ph
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The categories of this paper are: acc-phys
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The categories of this paper are: comp-gas
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The categories of this paper are: gr-qc
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The categories of this paper are: supr-con
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The categories of this paper are: hep-lat
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The categories of this paper are: nucl-ex
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The categories of this paper are: eess
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The categories of this paper are: q-alg
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The categories of this paper are: stat
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The categories of this paper are: hep-ex
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The categories of this paper are: ao-sci
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The categories of this paper are: q-fin