Deep Learning Interatomic Potential Connects Molecular Structural Ordering to Macroscale Properties of Polyacrylonitrile (PAN) Polymer

TL;DR

This paper introduces a neural network interatomic potential trained on small-scale ab initio data, enabling accurate large-scale modeling of PAN polymer's structure and properties, bridging molecular interactions to macroscale behavior.

Contribution

The study develops a neural network interatomic potential that extends ab initio accuracy from small molecules to large polymer systems, providing new insights into PAN's structure-property relationships.

Findings

NNIP accurately predicts PAN's amorphous structure and matches experimental X-ray data.

Predicted properties like density and elastic modulus agree with experiments.

Elastic modulus correlates strongly with molecular orientation in PAN.

Abstract

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.