{"675022":{"#nid":"675022","#data":{"type":"event","title":"PhD Defense by Joseph Kern","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003EJoseph Kern\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003EAdvisor: Prof. Ramprasad\u003Cbr\u003E\u003Cem\u003Ewill defend a doctoral thesis entitled\u003C\/em\u003E\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EDesign of (De)Polymerizable Polymers Using Machine Learning-Based Predictive Models and Generative Algorithms\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003E\u003Cem\u003EOn\u003C\/em\u003E\u003Cbr\u003E\u003Cstrong\u003EThursday, June 20th 2024 at 12:00 p.m.\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EMRDC Room 3515\u003C\/strong\u003E\u003Cbr\u003Eand Virtually via\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u003Ca href=\u0022https:\/\/teams.microsoft.com\/l\/meetup-join\/19%3ameeting_NDM2YjU2ZGQtMjZkYy00MDI4LWEwM2EtOTZhODBiZGNjMTBh%40thread.v2\/0?context=%7b%22Tid%22%3a%22482198bb-ae7b-4b25-8b7a-6d7f32faa083%22%2c%22Oid%22%3a%22d1c60b5a-458e-4f48-822d-566212880812%22%7d\u0022\u003EMS Teams\u003C\/a\u003E\u003C\/p\u003E\u003Cp\u003EMeeting ID: 253 298 189 29\u003C\/p\u003E\u003Cp\u003EPasscode: 4i2x7u\u003C\/p\u003E\u003Cp\u003E\u003Cbr\u003E\u003Cstrong\u003ECommittee\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003EDr. Rampi Ramprasad, Advisor, MSE\u003C\/p\u003E\u003Cp\u003EDr. Karl Jacob, MSE\u003C\/p\u003E\u003Cp\u003EDr. Sunderasan Jayaraman, MSE\u003C\/p\u003E\u003Cp\u003EDr. Blair Brettmann, ChBE\u003C\/p\u003E\u003Cp\u003EDr. Chao Zhang,\u0026nbsp; CSE\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EAbstract:\u0026nbsp;\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003EPlastics have become indispensable in our modern world, serving diverse purposes from packaging to electronics. However, the pervasive issue of plastic pollution, with microplastics now ubiquitous across the globe, poses serious threats to both environmental and human health. Despite this, conventional recycling methods often fall short due to cost constraints and technical challenges, necessitating a shift towards innovative, eco-friendly polymer solutions.\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EThis thesis delves into a vast chemical landscape, spanning hundreds of millions of commercially available and theoretical monomers\u2013a number impossible to explore experimentally\u2013in pursuit of novel polymers capable of addressing the shortcomings of traditional plastics. Employing digital reaction pathways, advanced genetic algorithms, and cutting-edge machine learning models, we aim to identify polymers that not only meet stringent recycling criteria but also possess the mechanical and thermal properties requisite for practical application.\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EOur exploration extends to predicting polymer solubility, streamlining experimental data analysis, and leveraging machine learning algorithms to assess monomer toxicity, thus refining the selection process. By navigating this intricate hypothetical polymer design space, we strive to provide insight to our polymer chemist collaborators, assisting them in uncovering the elusive \u0022needle in a haystack\u0022 polymer that could revolutionize the plastics industry and mitigate the global burden of plastic pollution.\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003E\u003Cstrong\u003EDesign of (De)Polymerizable Polymers Using Machine Learning-Based Predictive Models and Generative Algorithms\u003C\/strong\u003E\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Design of (De)Polymerizable Polymers Using Machine Learning-Based Predictive Models and Generative Algorithms"}],"uid":"27707","created_gmt":"2024-06-06 15:24:31","changed_gmt":"2024-06-06 15:25:01","author":"Tatianna Richardson","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2024-06-20T12:00:00-04:00","event_time_end":"2024-06-20T14:00:00-04:00","event_time_end_last":"2024-06-20T14:00:00-04:00","gmt_time_start":"2024-06-20 16:00:00","gmt_time_end":"2024-06-20 18:00:00","gmt_time_end_last":"2024-06-20 18:00:00","rrule":null,"timezone":"America\/New_York"},"location":"MRDC Room 3515 and Virtually via  MS Teams","extras":[],"groups":[{"id":"221981","name":"Graduate Studies"}],"categories":[],"keywords":[{"id":"100811","name":"Phd Defense"}],"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":""}}}