Ezio Iacocca, Assistant Professor in the Department of Physics and Energy Science, was recently named as an awardee for the prestigious Early Career Research Program grant from the Department of Energy.
Iacocca’s research is “Fourier and Fractional Neural Operators to Unveil Topological Textures in 3D Magnetism” and aims to identify more energy-efficient methods of data and information storage.
“The main goal is to discover new materials that can exceed the qualities of those found in nature,” Iacocca explained. “While our focus is on magnetic materials, the approaches investigated under this project can be used for materials science in general. One of the defining factors of our research is to give a physical meaning to the term ‘learning.’ We will use new physical models and formal analytical techniques to not only ‘learn’ and predict materials but do so by providing a physical sense of the prediction’s quality.”
“In other words, say the machine learning algorithm predicts spectacular characteristics for a material with periodic changes in its structure, then it is of fundamental interest to know how precise those changes must be,” added Iacocca. “Could a few defects destroy the characteristics? Can the predicted material be produced with state-of-the-art technology? These are some of the questions we expect to answer from our research.”
Iacocca is one of 93 early career scientists across the United States to receive the award, which totals $135 million in funding to be dispersed, and is eager to see where the research goes.
“I am very excited! It is deeply meaningful when other scientists find your ideas and research interesting and worth pursuing further, especially in a highly competitive award,” said Iacocca.
Another exciting aspect of this research is that it ties together several research avenues Iacocca has been interested in over the years, allowing him to bring different areas of expertise and study to the table.
“One of my recent research efforts have been devoted to the behavior of magnetic materials when subject to a femtosecond optical laser (a pulse of just 20 trillionths of a second),” Iacocca said. “By working on this research at UCCS, we understood that the models we are working on could be also applied to machine learning in a variety of ways, for example, to predict the material properties needed to stabilize complex textures in magnetic materials known as hopfions. We realized there was an open problem in the intersection of fields, and we were in a unique position to answer it. To me, this is what research is all about: gathering pieces of knowledge and different points of view to create new knowledge.”
Iacocca hopes to see this research positively affect and improve information technology.
“First and foremost, our goal is to advance science,” said Iacocca. “I expect our research to provide new analytical and numerical tools for the magnetism community capable of describing materials from atomic to micrometer length scales; and to close the gap between physics and machine learning by predicting materials that can be used for new paradigms in information technology.”
Iacocca also recognized his department team and those who’ve helped him in the submission process for their contributions.
“I would like to acknowledge the support from the Department of Physics and Energy Science by providing an outstanding research environment filled with discussions among faculty and student,” Iacocca noted. “In addition, nothing could have been achieved without the fantastic and meticulous support of Gwendolyn Gennaro and Whitney Porter at Offices of Sponsored Programs and Research Integrity (OSPRI) throughout this and many other submissions.”
Read more about the grant awardees and their research online, and visit the DOE Early Career Research Program page for more information on the program.
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