Accelerated design of proton exchange membranes for green hydrogen production with artificial intelligence

[2601.18914] Accelerated design of proton exchange membranes for green hydrogen production with artificial intelligence Skip to main content Search Submit Donate Log in Condensed Matter > Soft Condensed Matter arXiv:2601.18914 (cond-mat) [Submitted on 26 Jan 2026 ( v1 ), last revised 27 Aug 2026 (this version, v2)] Title: Accelerated design of proton exchange membranes for green hydrogen production with artificial intelligence Authors: Huan Tran , Akhlak Mahmood , Harshal Chaudhari , Kuldeep Mamtani , Chiho Kim , Rampi Ramprasad , Anand N. Krishnamoorthy , Abhirup Patra View PDF HTML (experimental) Abstract: Water electrolysis is an eco-friendly method for hydrogen production that has reached significant levels of technological maturity. Among commercialized water-electrolysis technologies, proton-exchange membrane electrolyzers offer high current density, fast dynamic response, and compact system design, among other advantages. On the other hand, managing their high capital cost and the ``forever-chemistry'' nature of Nafion, a perfluorinated proton-exchange membrane widely used in such devices, remains a major challenge. Searches for fluorine-free replacements for Nafion, pursued largely through physical experimentation, have been active for decades with limited success. In this work, we develop and demonstrate an AI-based strategy for designing proton-exchange membranes for electrolyzers. Two key components of this strategy are an implementation of the virtual forward-synthesis approach and a set of machine-learning predictive models for essential application-inspired membrane properties; the former generates a vast space of millions of synthesizable polymers, which are then evaluated and screened by the latter. The strategy is validated against experimental data for known membranes and then applied to design over 1700 synthesizable candidates. This article concludes with a forward-looking vision in which the strategy could be elevated into an interactive and iterative scheme that is based on large language models to facilitate materials design in multiple ways. Subjects: Soft Condensed Matter (cond-mat.soft) ; Materials Science (cond-mat.mtrl-sci) Cite as: arXiv:2601.18914 [cond-mat.soft] (or arXiv:2601.18914v2 [cond-mat.soft] for this version) https://doi.org/10.48550/arXiv.2601.18914 Focus to learn more arXiv-issued DOI via DataCite Journal reference: Chem. Mater. 38 (16), 8125-8134 (2026) Related DOI : https://doi.org/10.1021/acs.chemmater.6c00265 Focus to learn more DOI(s) linking to related resources Submission history From: Huan Tran [ view email ] [v1] Mon, 26 Jan 2026 19:24:34 UTC (7,599 KB) [v2] Thu, 27 Aug 2026 20:59:48 UTC (7,617 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Current browse context: cond-mat.soft < prev | next > new | recent | 2026-01 Change to browse by: cond-mat cond-mat.mtrl-sci References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) IArxiv recommender toggle IArxiv Recommender ( What is IArxiv? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. 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