A tedious process of trial and error is normally required to discover ceramic materials that generate electricity under mechanical stress. Known as piezoelectric ceramics, the valuable next-generation materials can be used to replace batteries in powering small electronic devices, like sensors. To streamline the research process, a team led by materials science researchers at Penn State introduced a new framework that combines an existing artificial intelligence (AI) model with human expertise to uncover these materials. To test the new approach, researchers developed a new piezoelectric composite and incorporated it into an energy harvester, which converts vibrations or movements into electricity.
The new approach, detailed in Nature Communications, comprises a three-phase research process, where an AI tool identifies possible material composites that are assessed by researchers before they put the selected potential composites to the test with hands-on experimentation. Researchers prompted a large language model to analyze patterns from decades of previously published research and yield a list of new material compositions.
"Imagine having an AI assistant that has read decades of scientific papers and can instantly suggest promising new materials," said Wesley Reinhart, assistant professor of materials science and engineering. "That is essentially what we created, but with scientists providing the critical physics, chemistry and engineering judgment needed to turn those suggestions into real-world technologies."
Using AI to identify promising candidates for experimentation frees up researchers to focus their efforts more on validation and optimizing materials for applications, according to Aman Nanda, doctoral student in materials science and engineering and first author of the study.
After creating a list of possible piezoelectric materials, researchers decided to focus on a potassium sodium niobate-based composition, abbreviated KNN-BNKZT-SCZ. The composite has a piezoelectric charge constant of approximately 440 picocoulombs per newton, meaning it can generate roughly three times more electrical output from the same amount of movement or vibration than conventional potassium sodium niobate-based materials.
Researchers further improved the material's performance through crystallographic texturing, a process that aligns grains within the ceramic to maximize its electrical properties. The textured material achieved an even higher electric charge constant of roughly 620 picocoulombs per newton while maintaining its ability to operate in high-temperature environments of up to 320 degrees Fahrenheit. The improved amount of voltage generated from a mechanical vibration made the material's performance competitive to existing materials, researchers said.
"This work demonstrates that AI is most powerful when it collaborates with scientists rather than replacing them," said Michael Lanagan, professor of engineering science and mechanics and co-author of the paper. "The AI program excels at identifying patterns across enormous amounts of scientific literature, but successful materials discovery still requires deep understanding of chemistry, crystal structures, processing and characterization. That partnership significantly helped develop the strategy for texturing, which showed significant improvement from 440 to 620 picocoulombs per newton."
To demonstrate the material's practical potential, the researchers incorporated KNN-BNKZT-SCZ into a magneto-mechano-electric energy harvester designed to convert mechanical and magnetic energy into usable electrical power. The device, which is typically used to power wireless sensors, achieved a power density of approximately 705 microwatts per cubic centimeter, outperforming comparable systems and the material's non-textured counterpart.
"Developing an outstanding material is only the first step; its true value lies in demonstrating reliable performance in a functional device," said Bed Poudel, research professor of materials science and engineering, and corresponding author of the study. "What makes this work particularly exciting is that we validated the material in a functional device. That gives us confidence that AI-guided discovery can accelerate the development of high-performance materials for practical applications."
Other co-authors on the work include Debjyoti Bhattacharya, doctoral student in materials science and engineering and a co-author of the paper; Shankar Kunwar, postdoctoral scholar in materials science and engineering; Shashank Priya, vice president for research and innovation at Michigan State University, who formerly served as Penn State's vice president for research and who earned a doctorate in materials science and engineering from Penn State; and Nayeon Kang and Jungho Ryu, both affiliated with Yeungnam University, South Korea.
The contributions to work by Penn State researchers were supported by the Office of Naval Research under award number N00014-22-1-2691 and the U.S. Army DEVCOM ARL Army Research Office under award number W911NF-23-2-0229. The full list of funders is available in the paper. This content is solely the responsibility of the authors and does not necessarily represent the views of the funders.
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