{"685577":{"#nid":"685577","#data":{"type":"event","title":"PhD Proposal by William Schertzer","body":[{"value":"\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EWilliam Schertzer\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003EAdvisor: Prof. Rampi\u0026nbsp;Ramprasad\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u003Cem\u003Ewill\u0026nbsp;propose\u0026nbsp;a doctoral\u0026nbsp;thesis\u0026nbsp;entitled,\u003C\/em\u003E\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EAI-Guided Investigation of Polymers for The Design of Robust Anion Exchange Membrane Fuel Cells\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003E\u003Cem\u003EOn\u003C\/em\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EThursday, Nov. 20, 2025\u003C\/p\u003E\u003Cp\u003E10 am - 12 pm\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u003Cem\u003EIn\u0026nbsp;\u003C\/em\u003E\u003C\/p\u003E\u003Cp\u003EMRDC Room 3515\u003C\/p\u003E\u003Cp\u003Eor\u0026nbsp;\u003C\/p\u003E\u003Cp\u003Evirtually via Teams:\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003ECommittee:\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003EProf. Rampi\u0026nbsp;Ramprasad- School of Materials Science and Engineering (advisor)\u003C\/p\u003E\u003Cp\u003EProf. Ryan P. Lively- School of Chemical and Biomolecular Engineering\u003C\/p\u003E\u003Cp\u003EProf. Chao Zhang- School of Computational Science and Engineering\u003C\/p\u003E\u003Cp\u003EProf. Scott Danielsen- School of Materials Science and Engineering\u003C\/p\u003E\u003Cp\u003EProf. Guoxiang (Emma) Hu- School of Materials Science and Engineering\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EAbstract:\u0026nbsp;\u003C\/strong\u003EAs the global demand for sustainable energy continues to rise, polymer-based anion exchange membranes (AEMs) have emerged as a promising platform for next-generation fuel cells that operate under alkaline conditions. However, the development of high-performance and durable AEMs is hindered by the vast design space of possible chemistries, the trade-offs among key transport and mechanical properties, and the scarcity of high-quality, structured experimental data. This thesis aims to accelerate the discovery, understanding, and lifetime prediction of AEM materials through a data-driven framework that integrates machine learning, physics-based modeling, and automated knowledge extraction from the scientific literature. The first part of this work establishes a computational pipeline for novel AEM copolymer design, where predictive models trained on curated literature data identify fluorine-free candidates with optimal combinations of hydroxide conductivity, water uptake, and swelling ratio. The second part introduces a physics\u003Cstrong\u003E-\u003C\/strong\u003Eenforced neural network (PENN\u003Cstrong\u003E)\u003C\/strong\u003E that learns universal degradation behavior across diverse AEM chemistries and operating conditions, enabling the forecasting of long-term conductivity decay (up to 10,000 h) from minimal early-time data. The final part of the thesis leverages optical\u003Cstrong\u003E\u0026nbsp;\u003C\/strong\u003Echaracter recognition, computer vision, large language models, and heuristics to automate the extraction of complex, context-rich data from figures, schematics, tables, and text within AEM literature. Together, these efforts will create a closed-loop platform for polymer discovery and degradation modeling, transforming how experimental knowledge is captured and applied to accelerate the design of sustainable, high-performance materials for clean energy technologies.\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003E\u003Cstrong\u003EAI-Guided Investigation of Polymers for The Design of Robust Anion Exchange Membrane Fuel Cells\u003C\/strong\u003E\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"AI-Guided Investigation of Polymers for The Design of Robust Anion Exchange Membrane Fuel Cells"}],"uid":"27707","created_gmt":"2025-10-07 18:44:51","changed_gmt":"2025-10-07 18:45:37","author":"Tatianna Richardson","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2025-11-20T10:00:00-05:00","event_time_end":"2025-11-20T12:00:00-05:00","event_time_end_last":"2025-11-20T12:00:00-05:00","gmt_time_start":"2025-11-20 15:00:00","gmt_time_end":"2025-11-20 17:00:00","gmt_time_end_last":"2025-11-20 17:00:00","rrule":null,"timezone":"America\/New_York"},"location":"MRDC Room 3515 or  virtually via Teams:","extras":[],"groups":[{"id":"221981","name":"Graduate Studies"}],"categories":[],"keywords":[{"id":"102851","name":"Phd proposal"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1788","name":"Other\/Miscellaneous"}],"invited_audience":[{"id":"78771","name":"Public"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}}}