AI Unlocks Atomic-Level Insights into Nanoparticle Behavior
A new study published in the journal Science details a groundbreaking method for visualizing the dynamic behavior of nanoparticles. These minuscule particles, which are building blocks in the creation of pharmaceuticals, electronics, and industrial products, have become a focus for researchers. The innovation combines the power of artificial intelligence with the precision of electron microscopy, offering an unprecedented view into how these tiny structures react to various stimuli.
“The nature of changes in the particle is exceptionally diverse, including fluxional periods, manifesting as rapid changes in atomic structure, particle shape, and orientation; understanding these dynamics requires new statistical tools,” explains David S. Matteson, a professor and associate chair in the Department of Statistics and Data Science at Cornell University, as well as director of the National Institute of Statistical Sciences, and a co-author of the study. “This study introduces a new statistic that utilizes topological data analysis to both quantify fluxionality and to track the stability of particles as they transition between ordered and disordered states.”
The research team included collaborators from New York University, Arizona State University, and the University of Iowa. The core of the development centers on marrying the capabilities of electron microscopy with advanced AI techniques. This combination allows scientists to observe the structures and movements of molecules at an incredibly small scale – a billionth of a meter – with remarkably enhanced time resolution.
“Nanoparticle-based catalytic systems have a tremendous impact on society,” states Carlos Fernandez-Granda, director of NYU’s Center for Data Science and a professor of mathematics and data science and one of the paper’s authors. “It is estimated that 90 percent of all manufactured products involve catalytic processes somewhere in their production chain. We have developed an artificial-intelligence method that opens a new window for the exploration of atomic-level structural dynamics in materials.”
Understanding the movement of atoms on a nanoparticle is critical for improving the functionality of various industrial applications. Previously, the atoms were barely visible in the data, making it difficult for scientists to study their behavior.
To overcome this obstacle, the researchers trained a deep neural network, a specialized AI system, to enhance electron microscope images. This technology allows scientists to reveal the underlying atomic structures and their dynamic behavior.
“Electron microscopy can capture images at a high spatial resolution, but because of the velocity at which the atomic structure of nanoparticles changes during chemical reactions, we need to gather data at a very high speed to understand their functionality,” notes Peter A. Crozier, a professor of materials science and engineering at Arizona State University and a co-author of the research. “This results in extremely noisy measurements. We have developed an artificial-intelligence method that learns how to remove this noise—automatically—enabling the visualization of key atomic-level dynamics.”
The National Science Foundation provided funding for the study.
