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Schertzer et al. J. Mater. Inf. 2025, 5, 5 Journal of
DOI: 10.20517/jmi.2024.69
Materials Informatics

Research Article Open Access

AI-driven design of fluorine-free polymers for
sustainable and high-performance anion exchange

membranes

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William Schertzer , Shivank Shukla , Abhishek Sose , Reanna Rafiq , Mohammed Al Otmi , Janani
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Sampath , Ryan P. Lively , Rampi Ramprasad 1,*
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Department of Materials Science and Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA.
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Department of Chemical Engineering, University of Florida, Gainesville, FL 32611, USA.
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Department of Chemical and Biomolecular Engineering, Georgia Institute of Technology, Atlanta, GA 30329, USA.
* Correspondence to: Dr. Rampi Ramprasad, Department of Materials Science and Engineering, Georgia Institute of Technology,
771 Ferst Drive NW, Atlanta, GA 30332, USA. E-mail: rampi.ramprasad@mse.gatech.edu
How to cite this article: Schertzer, W.; Shukla, S.; Sose, A.; Rafiq, R.; Otmi, M. A.; Sampath, J.; Lively, R. P.; Ramprasad, R. AI-
driven design of fluorine-free polymers for sustainable and high-performance anion exchange membranes. J. Mater. Inf. 2025, 5,
5. https://dx.doi.org/10.20517/jmi.2024.69
Received: 8 Nov 2024 First Decision: 3 Dec 2024 Revised: 16 Dec 2024 Accepted: 26 Dec 2024 Published: 16 Jan 2025
Academic Editors: Xingjun Liu, Runhai Ouyang, Rika Kobayashi Copy Editor: Pei-Yun Wang Production Editor: Pei-Yun Wang

Abstract
As global demand for clean energy increases, fuel cells have emerged as a key technology for sustainable power
generation. Anion exchange membrane (AEM) fuel cells offer a more economical and environmentally friendly
alternative to the popular proton exchange membrane (PEM) fuel cells, which rely on fluorinated polymers and also
use expensive platinum group catalysts. However, designing high-performance AEMs is challenging because of the
need to balance conflicting material properties. In this study, we employ machine learning to accelerate the design
of fluorine-free copolymers for AEMs, focusing on known monomer chemistries. By training models on AEM data
from the literature, we predicted key properties, namely, hydroxide ion conductivity, water uptake (WU), and
swelling ratio (SR). Screening 11 million novel copolymer candidates using predictive models and heuristic filters,
we identified more than 400 promising fluorine-free copolymer candidates with predicted OH conductivity greater
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than 100 mS/cm, WU below 35 wt%, and SR below 50%. This computational approach to AEM design could
contribute to developing more efficient and sustainable AEM fuel cells for various energy applications.

Keywords: Anion exchange membrane, fuel cells, anion conductivity, water uptake, swelling, materials informatics,
high-throughput polymer design

© The Author(s) 2025. Open Access This article is licensed under a Creative Commons Attribution 4.0
International License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, sharing,
adaptation, distribution and reproduction in any medium or format, for any purpose, even commercially, as
long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and
indicate if changes were made.

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