Machine
learning (ML) is having an increasing impact on the physical
sciences, engineering, and technology and its integration into molecular
simulation frameworks holds great potential to expand their scope
of applicability to complex materials and facilitate fundamental knowledge
and reliable property predictions, contributing to the development
of efficient materials design routes. The application of ML in materials
informatics in general, and polymer informatics in particular, has
led to interesting results, however great untapped potential lies
in the integration of ML techniques into the multiscale molecular
simulation methods for the study of macromolecular systems, specifically
in the context of Coarse Grained (CG) simulations. In this Perspective, we aim at presenting the pioneering recent
research efforts in this direction and discussing how these new ML-based
techniques can contribute to critical aspects of the development of
multiscale molecular simulation methods for bulk complex chemical
systems, especially polymers. Prerequisites for the implementation
of such ML-integrated methods and open challenges that need to be
met toward the development of general systematic ML-based coarse graining
schemes for polymers are discussed.