This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: A tedious process of trial and error is normally required to discover ceramic materials that generate electricity under mechanical stress. Known as piezoelectric ceramics, these valuable next-generation materials can replace batteries that power 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 an article published in Nature Communications, comprises a three-phase research process in which an AI tool identifies possible material composites that researchers assess before testing selected composites through hands-on experimentation.
Researchers prompted a large language model to analyze patterns from decades of previously published research and generate 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 researchers to focus more on validation and optimizing materials for applications, according to Aman Nanda, a 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°F (160°C). The increased voltage generated from mechanical vibration made the material's performance competitive with 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.
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