  Machine learning techniques including neural networks are popular tools for
materials and chemical scientists with applications that may provide viable
alternative methods in the analysis of structure and energetics of systems
ranging from crystals to biomolecules. However, efforts are less abundant for
prediction of dynamics. Here we explore the ability of three well established
recurrent neural network architectures for forecasting the energetics of a
macromolecular polymer-lipid aggregate solvated in ethyl acetate at ambient
conditions. Data models generated from recurrent neural networks are trained
and tested on nanoseconds-long time series of the intra-macromolecules
potential energy and their interaction energy with the solvent generated from
Molecular Dynamics and containing half million points. Our exhaustive analyses
convey that the three recurrent neural network investigated generate data
models with limited capability of reproducing the energetic fluctuations and
yielding short or long term energetics forecasts with underlying distribution
of points inconsistent with the input series distributions. We propose an in
silico experimental protocol consisting on forming an ensemble of artificial
network models trained on an ensemble of series with additional features from
time series containing pre-clustered time patterns of the original series. The
forecast process improves by predicting a band of forecasted time series with a
spread of values consistent with the molecular dynamics energy fluctuations
span. However, the distribution of points from the band of forecasts is not
optimal. Although the three inspected recurrent neural networks were unable of
generating single models that reproduce the actual fluctuations of the
inspected molecular system energies in thermal equilibrium at the nanosecond
scale, the proposed protocol provides useful estimates of the molecular fate
