  The properties of soft electronic materials depend on the coupling of
electronic and conformational degrees of freedom over a wide range of
spatiotemporal scales. Description of such properties requires multiscale
approaches capable of, at the same time, accessing electronic properties and
sampling the conformational space of soft materials. This could in principle be
realized by connecting the coarse-grained (CG) methodologies required for
adequate conformational sampling to conformationally-averaged electronic
property distributions via backmapping to atomistic-resolution level models and
repeated quantum-chemical calculations. Computational demands of such
approaches, however, have hindered their application in high-throughput
computer-aided soft materials discovery. Here, we present a method that,
combining machine learning and CG techniques, can replace traditional
backmapping-based approaches without sacrificing accuracy. We illustrate the
method for an emerging class of soft electronic materials, namely
non-conjugated, radical-containing polymers, promising materials for
all-organic energy storage. Supervised machine learning models are trained to
learn the dependence of electronic properties on polymer conformation at CG
resolutions. We then parametrize CG models that retain electronic structure
information, simulate CG condensed phases, and predict the electronic
properties of such phases solely from the CG degrees of freedom. We validate
our method by comparing it against a full backmapping-based approach, and find
good agreement between both methods. This work demonstrates the potential of
the proposed method to accelerate multiscale workflows, and provides a
framework for the development of CG models that retain electronic structure
information.
