Using AI to find the polymers of the future

Nylon, Teflon, Kevlar. These are just a few familiar polymers -- large-molecule chemical compounds -- 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.

Finding 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. Rampi Ramprasad's group develops and adapts AI algorithms to accelerate materials discovery.

This summer, two papers published in the Nature family of journals highlight the significant advancements and success stories emerging from years of AI-driven polymer informatics research. The first, featured in Nature Reviews Materials, showcases recent breakthroughs in polymer design across critical and contemporary application domains: energy storage, filtration technologies, and recyclable plastics. The second, published in Nature Communications, 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.

"In the early days of AI in materials science, propelled by the White House's Materials Genome Initiative over a decade ago, research in this field was largely curiosity-driven," said Ramprasad, a professor in the School of Materials Science and Engineering. "Only 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&D landscape. That's what makes this review so significant and timely."

AI Opportunities

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. The results from these new experiments are integrated with the original data, further refining the predictive models in a continuous, iterative process.

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. Ramprasad's group designs these materials, while their fabrication, processing, and testing are carried out by collaborators at various institutions, including Georgia Tech. Professor Ryan Lively from the School of Chemical and Biomolecular Engineering frequently collaborates with Ramprasad's group and is a co-author of the paper published in Nature Reviews Materials.

"In our day-to-day research, we extensively use the machine learning models Rampi's team has developed," Lively said. "These 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."

Using AI, Ramprasad's team and their collaborators have made significant advancements in diverse fields, including energy storage, filtration technologies, additive manufacturing, and recyclable materials.

Polymer Progress

One notable success, described in the Nature Communications 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's group worked with researchers from the University of Connecticut.

Current 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.

"The 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," said Ramprasad. "It 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."

Industry Potential

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. 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.

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Waste Styrofoam can now be converted into polymers for electronics

University of Delaware and Argonne National Laboratory have come up with a chemical reaction that can convert Styrofoam into a high-value conducting polymer known as PEDOT:PSS. In a new paper published in JACS Au, the study demonstrates how upgraded plastic waste can be successfully incorporated into functional electronic devices, including silicon-based hybrid solar cells and organic electrochemical transistors.

The research group of corresponding author Laure Kayser, assistant professor in the Department of Materials Science and Engineering in UD's College of Engineering with a joint appointment in the Department of Chemistry and Biochemistry in the College of Arts and Sciences, regularly works with PEDOT:PSS, a polymer that has both electronic and ionic conductivity, and was interested in finding ways to synthesize this material from plastic waste.

After connecting with Argonne chemist David Kaphan during an event hosted by UD's research office, the research teams at UD and Argonne began evaluating the hypothesis that PEDOT:PSS could be made by sulfonating polystyrene, a synthetic plastic found in many types of disposable containers and packing materials.

Sulfonation is a common chemical reaction where a hydrogen atom is replaced by sulfonic acid; the process is used to create a variety of products such as dyes, drugs and ion exchange resins. These reactions can either be "hard" (with higher conversion efficiency but that require caustic reagents) or "soft" (a less efficient method but one that uses milder materials).

In this paper, the researchers wanted to find something in the middle: "A reagent that is efficient enough to get really high degrees of functionalization but that doesn't mess up your polymer chain," Kayser explained.

The researchers first turned to a method described in a previous study for sulfonating small molecules, one that showed promising results in terms of efficiency and yield, using 1,3-Disulfonic acid imidazolium chloride ([Dsim]Cl). But adding functional groups onto a polymer is more challenging than for a small molecule, the researchers explained, because not only are unwanted byproducts harder to separate, any small errors in the polymer chain can change its overall properties.

To address this challenge, the researchers embarked on many months of trial and error to find the optimal conditions that minimized side reactions, said Kelsey Koutsoukos, a materials science doctoral candidate and second author of this paper.

"We screened different organic solvents, different molar ratios of the sulfonating agent, and evaluated different temperatures and times to see which conditions were the best for achieving high degrees of sulfonation," he said.

The researchers were able to find reaction conditions that resulted in high polymer sulfonation, minimal defects and high efficiency, all while using a mild sulfonating agent. And because the researchers were able to use polystyrene, specifically waste Styrofoam, as a starting material, their method also represents an efficient way to convert plastic waste into PEDOT:PSS.

Once the researchers had PEDOT:PSS in hand, they were able to compare how their waste-derived polymer performed compared to commercially available PEDOT:PSS.

"In this paper, we looked at two devices -- an organic electronic transistor and a solar cell," said Chun-Yuan Lo, a chemistry doctoral candidate and the paper's first author. "The performance of both types of conductive polymers was comparable, and shows that our method is a very eco-friendly approach for converting polystyrene waste into high-value electronic materials."

Specific analyses conducted at UD included X-ray photoelectron spectroscopy (XPS) at the surface analysis facility, film thickness analysis at the UD Nanofabrication Facility, and solar cell evaluation at the Institute of Energy Conversion. Argonne's advanced spectroscopy equipment, such as carbon NMR, was used for detailed polymer characterization. Additional support was provided by materials science and engineering professor Robert Opila for solar cell analysis and by David C. Martin, the Karl W. and Renate Böer Chaired Professor of Materials Science and Engineering, for the electronic device performance analyses.

One unexpected finding related to the chemistry, the researchers added, is the ability to use stoichiometric ratios during the reaction.

"Typically, for sulfonation of polystyrene, you have to use an excess of really harsh reagents. Here, being able to use a stoichiometric ratio means that we can minimize the amount of waste being generated," Koutsoukos said.

This finding is something the Kayser group will be looking into further as a way to "fine-tune" the degree of sulfonation. So far, they've found that by varying the ratio of starting materials, they can change the degree of sulfonation on the polymer. Along with studying how this degree of sulfonation impacts the electrical properties of PEDOT:PSS, the team is interested in seeing how this fine-tuning capability can be used for other applications, such as fuel cells or water filtration devices, where the degree of sulfonation greatly impacts a material's properties.

"For the electronic devices community, the key takeaway is that you can make electronic materials from trash, and they perform just as well as what you would purchase commercially," Kayser said. "For the more traditional polymer scientists, the fact that you can very efficiently and precisely control the degree of sulfonation is going to be of interest to a lot of different communities and applications."

The researchers also see great potential for how this research can contribute to ongoing global sustainability efforts by providing a new way to convert waste products into value-added materials.

"Many scientists and researchers are working hard on upcycling and recycling efforts, either by chemical or mechanical means, and our study provides another example of how we can address this challenge," Lo said.

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Printed robots with bones, ligaments, and tendons

3D printing is advancing rapidly, and the range of materials that can be used has expanded considerably. While the technology was previously limited to fast-curing plastics, it has now been made suitable for slow-curing plastics as well. These have decisive advantages as they have enhanced elastic properties and are more durable and robust.

The use of such polymers is made possible by a new technology developed by researchers at ETH Zurich and a US start-up. As a result, researchers can now 3D print complex, more durable robots from a variety of high-quality materials in one go. This new technology also makes it easy to combine soft, elastic, and rigid materials. The researchers can also use it to create delicate structures and parts with cavities as desired.

Materials that return to their original state

Using the new technology, researchers at ETH Zurich have succeeded for the first time in printing a robotic hand with bones, ligaments and tendons made of different polymers in one go. "We wouldn't have been able to make this hand with the fast-curing polyacrylates we've been using in 3D printing so far," explains Thomas Buchner, a doctoral student in the group of ETH Zurich robotics professor Robert Katzschmann and first author of the study. "We're now using slow-curing thiolene polymers. These have very good elastic properties and return to their original state much faster after bending than polyacrylates." This makes thiolene polymers ideal for producing the elastic ligaments of the robotic hand.

In addition, the stiffness of thiolenes can be fine-tuned very well to meet the requirements of soft robots. "Robots made of soft materials, such as the hand we developed, have advantages over conventional robots made of metal. Because they're soft, there is less risk of injury when they work with humans, and they are better suited to handling fragile goods," Katzschmann explains.

Scanning instead of scraping

3D printers typically produce objects layer by layer: nozzles deposit a given material in viscous form at each point; a UV lamp then cures each layer immediately. Previous methods involved a device that scraped off surface irregularities after each curing step. This works only with fast-curing polyacrylates. Slow-curing polymers such as thiolenes and epoxies would gum up the scraper.

To accommodate the use of slow-curing polymers, the researchers developed 3D printing further by adding a 3D laser scanner that immediately checks each printed layer for any surface irregularities. "A feedback mechanism compensates for these irregularities when printing the next layer by calculating any necessary adjustments to the amount of material to be printed in real time and with pinpoint accuracy," explains Wojciech Matusik, a professor at the Massachusetts Institute of Technology (MIT) in the US and co-author of the study. This means that instead of smoothing out uneven layers, the new technology simply takes the unevenness into account when printing the next layer.

Inkbit, an MIT spin-off, was responsible for developing the new printing technology. The ETH Zurich researchers developed several robotic applications and helped optimise the printing technology for use with slow-curing polymers. The researchers from Switzerland and the US have now jointly published the technology and their sample applications in the journal Nature.

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