Researchers at Georgia Tech are harnessing artificial intelligence to revolutionize the field of polymer science, which has given us game-changing materials like Nylon, Teflon, and Kevlar. Led by Professor Rampi Ramprasad, the team is developing AI algorithms to accelerate the discovery of new polymers with specific properties. This summer, two papers in Nature journals highlighted their breakthroughs in designing polymers for energy storage, filtration technologies, and recyclable plastics.
Ramprasad’s group has created algorithms that can predict polymer properties before they’re even made, and generate new polymers that meet target criteria. Collaborators at institutions like the University of Connecticut have validated these predictions through laboratory synthesis and testing. The team’s work has led to significant advancements in fields like energy storage, additive manufacturing, and recyclable materials.
Key players include Professor Ryan Lively from Georgia Tech’s School of Chemical and Biomolecular Engineering, and researchers from Toyota Research Institute and General Electric. Ramprasad has also co-founded Matmerize Inc., a software startup that provides cloud-based polymer informatics tools to companies across various sectors.
Accelerating Polymer Discovery with Artificial Intelligence
Polymers, large-molecule chemical compounds, have revolutionized various industries and transformed the way we live. From Teflon-coated frying pans to 3D printing, polymers play a vital role in creating systems that make our world function better. However, finding the next groundbreaking polymer is always a challenge. Researchers at Georgia Tech are now using artificial intelligence (AI) to shape and transform the future of the field.
Rampi Ramprasad’s group develops and adapts AI algorithms to accelerate materials discovery. This summer, two papers published in the Nature family of journals highlighted significant advancements and success stories emerging from years of AI-driven polymer informatics research. The first paper, featured in Nature Reviews Materials, showcased recent breakthroughs in polymer design across critical and contemporary application domains: energy storage, filtration technologies, and recyclable plastics. The second paper, published in Nature Communications, focused on the use of AI algorithms to discover a subclass of polymers for electrostatic energy storage.
AI Opportunities in Polymer Discovery
Ramprasad’s 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.
While 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. The 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.
Polymer Progress: Success Stories in Energy Storage and Beyond
One notable success 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’s group worked with researchers from the University of Connecticut to determine that insulating materials made from polynorbornene and polyimide polymers can simultaneously achieve high energy density and high thermal stability.
The new class of polymers is one of the most concrete examples of how AI can guide materials discovery. This achievement is also the result of years of multidisciplinary collaborative work with Greg Sotzing and Yang Cao at the University of Connecticut, as well as sustained sponsorship by the Office of Naval Research.
Industry Potential: Translating AI-Assisted Materials Development into Real-World Applications
The potential for real-world translation of AI-assisted materials development is underscored by industry participation in the Nature Reviews Materials 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 Matmerize Inc., a software startup company recently spun out of Georgia Tech.
Matmerize’s cloud-based polymer informatics software is already being used by companies across various sectors, including energy, electronics, consumer products, chemical processing, and sustainable materials. This software has transformed research into a robust, versatile, and industry-ready solution, enabling users to design materials virtually with enhanced efficiency and reduced cost. What began as a curiosity has gained significant momentum, and we are entering an exciting new era of materials by design.
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