AI-Assisted Physics-Informed Predictions of Degradation Behavior of Polymeric Anion Exchange Membranes
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866909846083731456 |
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| author | Schertzer, William Otmi, Mohamed Al Sampath, Janani Lively, Ryan P. Ramprasad, Rampi |
| author_facet | Schertzer, William Otmi, Mohamed Al Sampath, Janani Lively, Ryan P. Ramprasad, Rampi |
| contents | The global transition to hydrogen-based energy infrastructures faces significant hurdles. Chief among these are the high costs and sustainability issues associated with acid-based proton exchange membrane fuel cells. Anion exchange membrane (AEM) fuel cells offer promising cost-effective alternatives, yet their widespread adoption is limited by rapid degradation in alkaline environments. Here, we develop a framework that integrates mechanistic insights with machine learning, enabling the identification of generalized degradation behavior across diverse polymeric AEM chemistries and operating conditions. Our model successfully predicts long-term hydroxide conductivity degradation (up to 10,000 hours) from minimal early-time experimental data. This capability significantly reduces experimental burdens and may expedite the design of high-performance, durable AEM materials. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_12655 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AI-Assisted Physics-Informed Predictions of Degradation Behavior of Polymeric Anion Exchange Membranes Schertzer, William Otmi, Mohamed Al Sampath, Janani Lively, Ryan P. Ramprasad, Rampi Soft Condensed Matter The global transition to hydrogen-based energy infrastructures faces significant hurdles. Chief among these are the high costs and sustainability issues associated with acid-based proton exchange membrane fuel cells. Anion exchange membrane (AEM) fuel cells offer promising cost-effective alternatives, yet their widespread adoption is limited by rapid degradation in alkaline environments. Here, we develop a framework that integrates mechanistic insights with machine learning, enabling the identification of generalized degradation behavior across diverse polymeric AEM chemistries and operating conditions. Our model successfully predicts long-term hydroxide conductivity degradation (up to 10,000 hours) from minimal early-time experimental data. This capability significantly reduces experimental burdens and may expedite the design of high-performance, durable AEM materials. |
| title | AI-Assisted Physics-Informed Predictions of Degradation Behavior of Polymeric Anion Exchange Membranes |
| topic | Soft Condensed Matter |
| url | https://arxiv.org/abs/2510.12655 |