Concerted Electron-Ion Transport by Polyacrylonitrile Elucidated with Reactive Deep Learning Potentials

TL;DR

This study uses reactive deep learning potentials to elucidate the mechanisms of charge transport in polyacrylonitrile, revealing how nucleophile attack initiates rapid ring-formation and electron transfer, with implications for energy storage materials.

Contribution

We developed a deep-learning potential trained on ab initio data to uncover the reactive kinetics and charge transport mechanisms in PAN, a novel approach for studying complex polymer reactions.

Findings

Nucleophile attack is the rate-limiting step in PAN cyclization.

Sequential nitrile ring formation is ~10,000 times faster after initial attack.

Validated computational pathway with IR and NMR experiments.

Abstract

Charge transport in polymers, such as polyacrylonitrile (PAN), is crucial for electronics and energy storage. For instance, PAN can transport cations e.g., Li+, by facilitating dynamic cation-nitrile coordination in batteries. However, little is known regarding the underlying role of complex reactive polymer configurations. Herein, we develop a deep-learning potential, trained on ab initio energies and forces of nonequilibrium reactive PAN configurations, to unravel the kinetics of PAN cyclization initiated by a nucleophile (OH- dissociated from LiOH) attacking the terminal nitrile carbon. We find, based on the reaction free-energetics, rates, and charge analysis, that the nucleophile attack producing the first ring is the rate-limiting step, which subsequently triggers Li+-coupled electron transfer along the PAN backbone, causing ~10,000 times faster sequential ring-formation of the remaining nitriles. PAN's extended configurations, where dipolar and H-bonding interactions are minimal, enable such rapid kinetics. By validating our computational findings with IR and NMR experiments, we establish a pathway for designing reactive polymers with enhanced charge transport for energy applications.