  Polyacrylonitrile (PAN) is an important commercial polymer, bearing atactic
stereochemistry resulting from nonselective radical polymerization. As such, an
accurate, fundamental understanding of governing interactions among PAN
molecular units are indispensable to advance the design principles of final
products at reduced processability costs. While ab initio molecular dynamics
(AIMD) simulations can provide the necessary accuracy for treating key
interactions in polar polymers such as dipole-dipole interactions and hydrogen
bonding, and analyzing their influence on molecular orientation, their
implementation is limited to small molecules only. Herein, we show that the
neural network interatomic potentials (NNIP) that are trained on the
small-scale AIMD data (acquired for oligomers) can be efficiently employed to
examine the structures/properties at large scales (polymers). NNIP provides
critical insight into intra- and interchain hydrogen bonding and dipolar
correlations, and accurately predicts the amorphous bulk PAN structure
validated by modeling the experimental X-ray structure factor. Furthermore, the
NNIP-predicted PAN properties such as density and elastic modulus are in good
agreement with their experimental values. Overall, the trend in the elastic
modulus is found to correlate strongly with the PAN structural orientations
encoded in Hermans orientation factor. This study enables the ability to
predict the structure-property relations for PAN and analogs with sustainable
ab initio accuracy across scales.
