LEONARDO is the first engineered AI to be deployed to unlock the intricacies of nanoparticle motion in complex liquid environments. This is a major step forward for the field of materials science. This breakthrough comes from Zain Shabeeb, a Ph.D. student at the School of Chemical and Biomolecular Engineering at Georgia Tech (ChBE@GT), in collaboration with assistant professor Vida Jamali. Their collaborative, interdisciplinary work was published in the highly esteemed journal, Nature Communications. It demonstrates how LEONARDO harvests and analyzes enormous experimental data to constantly improve simulations of realistic nanoparticle behavior.
LEONARDO runs on an advanced workflow that fuses a process of experimental data collection with a deep learning framework. By analyzing a staggering 38,000 short trajectories under varying conditions—including different particle sizes, frame rates, and electron beam settings—LEONARDO learns to predict and generate realistic motion patterns. This unique capacity to synthesize data-driven insights empowers researchers to go beyond observation and into the realm of simulation.
Understanding Nanoparticle Dynamics
Nanoparticles reside in a world of perpetual Brownian motion and their behavior can be anything but straightforward. They bounce around, tumble, roll and tumble all over four dimensional fluid dynamic environments. Their movements are not random—they are guided by viscoelastic fluids, energy barriers, and surface interactions. Common, traditional models such as Brownian motion just aren’t up to the task. They’re unable to fully reproduce the intricacy of these movements. In this context, LEONARDO fills this gap by using machine learning to generate better data-based representations of nanoparticle dynamics.
The development of this tool is informed not just by data collected in the real-world, but by physics-based principles. This hybrid approach allows it to produce motion trajectories that are nearly indistinguishable from those we find in our human-realistic experiments. Scientists can now use LEONARDO to explore various scenarios and conditions, gaining insights that were previously difficult or impossible to attain.
Collaborative Innovation
The partnership between Shabeeb and Jamali has been instrumental in making LEONARDO happen. As an assistant professor and Daniel B. Mowrey Faculty Fellow at ChBE@GT, Jamali’s expertise will serve as a strong complement to Shabeeb’s research efforts. They’ve paired experimental know-how with state-of-the-art AI technology. This groundbreaking collaboration has produced a tool that significantly improves our understanding of how nanoparticles behave.
Jamali illustrated the value research has in collaboration, both within her research and beyond. She added that LEONARDO represents a major leap forward for scientists researching potential impacts of nanoparticles. This unprecedented power to recreate complex motion trajectories in a realistic setup promises to inspire a wave of research and experimentation within the field.
Future Implications
The ramifications of LEONARDO go beyond the scope of academic interest. They have the potential to transform fields from nanotechnology to materials science to biology. Currently, though, scientists are able to understand—and predict—nanoparticle dynamics much more accurately. This breakthrough allows them to create more exact materials and processes across disciplines, from drug delivery systems to energy storage solutions.
Researchers are still investigating some critical potential applications of LEONARDO. This pioneering technology holds potential to reveal much greater detail about how nanoparticles are likely to behave in a variety of conditions. This would result in a quantum leap forward in the way that materials are designed and used in a variety of industries.