  A tight and consistent link between resolutions is crucial to further expand
the impact of multiscale modeling for complex materials. We herein tackle the
generation of condensed molecular structures as a refinement -- backmapping --
of a coarse-grained structure. Traditional schemes start from a rough
coarse-to-fine mapping and perform further energy minimization and molecular
dynamics simulations to equilibrate the system. In this study we introduce
DeepBackmap: A deep neural network based approach to directly predict
equilibrated molecular structures for condensed-phase systems. We use
generative adversarial networks to learn the Boltzmann distribution from
training data and realize reverse mapping by using the coarse-grained structure
as a conditional input. We apply our method to a challenging condensed-phase
polymeric system. We observe that the model trained in a melt has remarkable
transferability to the crystalline phase. The combination of data-driven and
physics-based aspects of our architecture help reach temperature
transferability with only limited training data.
