Researchers from Georgia Tech are using artificial intelligence (AI) to find the next groundbreaking polymer. Rampi Ramprasad's group develops and adapts AI algorithms to accelerate materials discovery. Two research 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 using AI algorithms to discover a subclass of polymers for electrostatic energy storage, with the designed materials undergoing successful laboratory synthesis and testing.

According to Ramprasad, a professor in the School of Materials Science and Engineering, 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. He opines only in recent years have they 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 possibilities

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 properties 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.

Overcoming challenges

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 complex.

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 co-authors the paper published in Nature Reviews Materials.

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

Phys Org