Accelerated design of proton exchange membranes for green hydrogen production with artificial intelligence
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866915757398425600 |
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| author | Tran, Huan Mahmood, Akhlak Chaudhari, Harshal Mamtani, Kuldeep Kim, Chiho Ramprasad, Rampi Krishnamoorthy, Anand N. Patra, Abhirup |
| author_facet | Tran, Huan Mahmood, Akhlak Chaudhari, Harshal Mamtani, Kuldeep Kim, Chiho Ramprasad, Rampi Krishnamoorthy, Anand N. Patra, Abhirup |
| contents | 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 new 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 1,700 new synthesizable candidates. This article concludes with a forward-looking vision in which the strategy could be elevated into an interactive and iterative scheme that are based on large language models to facilitate materials design in multiple ways. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_18914 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Accelerated design of proton exchange membranes for green hydrogen production with artificial intelligence Tran, Huan Mahmood, Akhlak Chaudhari, Harshal Mamtani, Kuldeep Kim, Chiho Ramprasad, Rampi Krishnamoorthy, Anand N. Patra, Abhirup Soft Condensed Matter Materials Science 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 new 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 1,700 new synthesizable candidates. This article concludes with a forward-looking vision in which the strategy could be elevated into an interactive and iterative scheme that are based on large language models to facilitate materials design in multiple ways. |
| title | Accelerated design of proton exchange membranes for green hydrogen production with artificial intelligence |
| topic | Soft Condensed Matter Materials Science |
| url | https://arxiv.org/abs/2601.18914 |