  Due to the wide range of timescales that are present in macromolecular
systems, hierarchical multiscale strategies are necessary for their
computational study. Coarse-graining (CG) allows to establish a link between
different system resolutions and provides the backbone for the development of
robust multiscale simulations and analyses. The CG mapping process is typically
system- and application-specific, and it relies on chemical intuition. In this
work, we explored the application of a Machine Learning strategy, based on
Variational Autoencoders, for the development of suitable mapping schemes from
the atomistic to the coarse-grained space of molecules with increasing chemical
complexity. An extensive evaluation of the effect of the model hyperparameters
on the training process and on the final output was performed, and an existing
method was extended with the definition of different loss functions and the
implementation of a selection criterion that ensures physical consistency of
the output. The relationship between the input feature choice and the
reconstruction accuracy was analyzed, supporting the need to introduce
rotational invariance into the system. Strengths and limitations of the
approach, both in the mapping and in the backmapping steps, are highlighted and
critically discussed.
