{"676096":{"#nid":"676096","#data":{"type":"news","title":"Using AI to Find the Polymers of the Future","body":[{"value":"\u003Cp\u003ENylon, Teflon, Kevlar. These are just a few familiar polymers \u2014 large-molecule chemical compounds \u2014 that have changed the world. From Teflon-coated frying pans to 3D printing, polymers are vital to creating the systems that make the world function better.\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EFinding the next groundbreaking polymer is always a challenge, but now Georgia Tech researchers are using artificial intelligence (AI) to shape and transform the future of the field.\u0026nbsp;\u003Ca href=\u0022https:\/\/www.mse.gatech.edu\/people\/rampi-ramprasad\u0022\u003ERampi Ramprasad\u2019s\u003C\/a\u003E group develops and adapts AI algorithms to accelerate materials discovery.\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EThis summer, two papers published in the \u003Cem\u003ENature\u003C\/em\u003E family of journals highlight the significant advancements and success stories emerging from years of AI-driven polymer informatics research. The first, featured in \u003Ca href=\u0022https:\/\/www.nature.com\/articles\/s41578-024-00708-8\u0022\u003E\u003Cem\u003ENature Reviews Materials\u003C\/em\u003E\u003C\/a\u003E, showcases recent breakthroughs in polymer design across critical and contemporary application domains: energy storage, filtration technologies, and recyclable plastics. The second, published in\u0026nbsp;\u003Ca href=\u0022https:\/\/www.nature.com\/articles\/s41467-024-50413-x\u0022\u003E\u003Cem\u003ENature Communications\u003C\/em\u003E\u003C\/a\u003E, focuses on the use of AI algorithms to discover a subclass of polymers for electrostatic energy storage, with the designed materials undergoing successful laboratory synthesis and testing.\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u201cIn the early days of AI in materials science, propelled by the White House\u2019s Materials Genome Initiative over a decade ago, research in this field was largely curiosity-driven,\u201d said Ramprasad, a professor in the\u0026nbsp;\u003Ca href=\u0022https:\/\/www.mse.gatech.edu\/\u0022\u003ESchool of Materials Science and Engineering\u003C\/a\u003E. \u201cOnly in recent years have we begun to see tangible, real-world success stories in AI-driven accelerated polymer discovery. These successes are now inspiring significant transformations in the industrial materials R\u0026amp;D landscape. That\u2019s what makes this review so significant and timely.\u201d\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EAI Opportunities\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003ERamprasad\u2019s team has developed groundbreaking algorithms that can instantly predict polymer properties and formulations before they are physically created. The process begins by defining application-specific target property or performance criteria. Machine learning (ML) models train on existing material-property data to predict these desired outcomes. Additionally, the team can generate new polymers, whose properties are forecasted with ML models. The top candidates that meet the target property criteria are then selected for real-world validation through laboratory synthesis and testing. The results from these new experiments are integrated with the original data, further refining the predictive models in a continuous, iterative process.\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EWhile AI can accelerate the discovery of new polymers, it also presents unique challenges. The accuracy of AI predictions depends on the availability of rich, diverse, extensive initial data sets, making quality data paramount. Additionally, designing algorithms capable of generating chemically realistic and synthesizable polymers is a complex task.\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EThe real challenge begins after the algorithms make their predictions: proving that the designed materials can be made in the lab and function as expected and then demonstrating their scalability beyond the lab for real-world use. Ramprasad\u2019s group designs these materials, while their fabrication, processing, and testing are carried out by collaborators at various institutions, including Georgia Tech. Professor \u003Ca href=\u0022https:\/\/www.chbe.gatech.edu\/directory\/person\/ryan-lively\u0022\u003ERyan Lively\u003C\/a\u003E from the\u0026nbsp;\u003Ca href=\u0022https:\/\/www.chbe.gatech.edu\/\u0022\u003ESchool of Chemical and Biomolecular Engineering\u003C\/a\u003E frequently collaborates with Ramprasad\u2019s group and is a co-author of the paper published in \u003Cem\u003ENature Reviews Materials\u003C\/em\u003E.\u003C\/p\u003E\u003Cp\u003E\u0022In our day-to-day research, we extensively use the machine learning models Rampi\u2019s team has developed,\u201d Lively said. \u201cThese tools accelerate our work and allow us to rapidly explore new ideas. This embodies the promise of ML and AI because we can make model-guided decisions before we commit time and resources to explore the concepts in the laboratory.\u0022\u003C\/p\u003E\u003Cp\u003EUsing AI, Ramprasad\u2019s team and their collaborators have made significant advancements in diverse fields, including energy storage, filtration technologies, additive manufacturing, and recyclable materials.\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EPolymer Progress\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003EOne notable success, described in the \u003Cem\u003ENature Communications\u003C\/em\u003E paper, involves the design of new polymers for capacitors, which store electrostatic energy. These devices are vital components in electric and hybrid vehicles, among other applications. Ramprasad\u2019s group worked with researchers from the University of Connecticut.\u003C\/p\u003E\u003Cp\u003ECurrent capacitor polymers offer either high energy density or thermal stability, but not both. By leveraging AI tools, the researchers determined that insulating materials made from polynorbornene and polyimide polymers can simultaneously achieve high energy density and high thermal stability. The polymers can be further enhanced to function in demanding environments, such as aerospace applications, while maintaining environmental sustainability.\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u201cThe new class of polymers with high energy density and high thermal stability is one of the most concrete examples of how AI can guide materials discovery,\u201d said Ramprasad. \u201cIt is also the result of years of multidisciplinary collaborative work with Greg Sotzing and Yang Cao at the University of Connecticut and sustained sponsorship by the Office of Naval Research.\u201d\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EIndustry Potential\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003EThe potential for real-world translation of AI-assisted materials development is underscored by industry participation in the \u003Cem\u003ENature Reviews Materials\u003C\/em\u003E article. Co-authors of this paper also include scientists from Toyota Research Institute and General Electric. To further accelerate the adoption of AI-driven materials development in industry, Ramprasad co-founded\u0026nbsp;\u003Ca href=\u0022https:\/\/www.matmerize.com\/\u0022\u003EMatmerize Inc\u003C\/a\u003E., a software startup company recently spun out of Georgia Tech. Their cloud-based polymer informatics software is already being used by companies across various sectors, including energy, electronics, consumer products, chemical processing, and sustainable materials.\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u201cMatmerize has transformed our research into a robust, versatile, and industry-ready solution, enabling users to design materials virtually with enhanced efficiency and reduced cost,\u201d Ramprasad said. \u201cWhat began as a curiosity has gained significant momentum, and we are entering an exciting new era of materials by design.\u201d\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EFinding the next groundbreaking polymer is always a challenge, but now Georgia Tech researchers are using artificial intelligence (AI) to shape and transform the future of the field.\u0026nbsp;\u003Ca href=\u0022https:\/\/www.mse.gatech.edu\/people\/rampi-ramprasad\u0022\u003ERampi Ramprasad\u2019s\u003C\/a\u003E group develops and adapts AI algorithms to accelerate materials discovery.\u0026nbsp;\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"The research group published two Nature papers on their decades of work in the area."}],"uid":"34541","created_gmt":"2024-08-19 20:41:08","changed_gmt":"2024-08-29 16:17:54","author":"Tess Malone","boilerplate_text":"","field_publication":"","field_article_url":"","dateline":{"date":"2024-08-19T00:00:00-04:00","iso_date":"2024-08-19T00:00:00-04:00","tz":"America\/New_York"},"extras":[],"hg_media":{"674636":{"id":"674636","type":"image","title":"RRGroup_2023.png","body":null,"created":"1724101057","gmt_created":"2024-08-19 20:57:37","changed":"1724101057","gmt_changed":"2024-08-19 20:57:37","alt":"Rampi Ramprasad\u0027s research group","file":{"fid":"258186","name":"RRGroup_2023.png","image_path":"\/sites\/default\/files\/2024\/08\/19\/RRGroup_2023_0.png","image_full_path":"http:\/\/hg.gatech.edu\/\/sites\/default\/files\/2024\/08\/19\/RRGroup_2023_0.png","mime":"image\/png","size":1286097,"path_740":"http:\/\/hg.gatech.edu\/sites\/default\/files\/styles\/740xx_scale\/public\/2024\/08\/19\/RRGroup_2023_0.png?itok=mXMo-1Dr"}}},"media_ids":["674636"],"groups":[{"id":"660369","name":"Matter and Systems"}],"categories":[],"keywords":[{"id":"187915","name":"go-researchnews"}],"core_research_areas":[{"id":"193658","name":"Commercialization"},{"id":"39471","name":"Materials"},{"id":"193652","name":"Matter and Systems"}],"news_room_topics":[],"event_categories":[],"invited_audience":[],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[{"value":"\u003Cp\u003ETess Malone, Senior Research Writer\/Editor\u003C\/p\u003E\u003Cp\u003Etess.malone@gatech.edu\u003C\/p\u003E","format":"limited_html"}],"email":[],"slides":[],"orientation":[],"userdata":""}}}