A deep learning protocol for analyzing and predicting ionic conductivity of anion exchange membranes

Possessing high ionic conductivity is required to polymer-based membrane electrolytes. However, it is a challenge to evaluate the conductivity based on the structure of the polymer membrane without any measurements. We present a deep learning protocol to predict the hydroxide ion (OH-) conductivity from chemical structure information of poly (2,6-dimethyl phenylene oxide)-based anion exchange membranes (AEMs) grafting with one kind of functional cationic group. The modeling process includes data collection and feature processing, functional cationic group identification, OH- conductivity prediction and scientific law extraction. The established model achieves 99.7% of accuracy for classifying various functional cationic groups. The prediction error in OH -conductivity is +/- 0.016 S/cm for quaternary ammonium based AEMs, +/- 0.014 S/cm for saturated heterocyclic ammonium based ones, and +/- 0.07 S/cm for those possessing imidazolium cations. The proposed protocol is powerful to assist researchers in designing the AEMs with predictable OH- conductivity, and provides a new research paradigm of the AEMs preparation.

Automated summary

This study introduces a deep learning protocol to predict the OH- conductivity of polymer membrane based on its structure, achieving 99.7% accuracy in classifying different functional cationic groups and providing a powerful tool for designing AEMs with predictable conductivity. The prediction errors for different types of AEMs are within a reasonable range, demonstrating the effectiveness of the proposed protocol in assisting researchers in AEMs preparation.