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Two ÃÛèÖÊÓƵ physicists are advancing the frontiers of sub-atomic research—from the way nuclei are put together to the life cycle of stars—by developing artificial intelligence (AI) and computational tools to accelerate both theoretical and experimental research.

Christian Drischler and Daniel Phillips are playing a key role in a worldwide push to use new tools in the search for answers to some of physics’ biggest questions.

One project is taking on machine learning and AI, and the other has already released two versions of software to conduct Bayesian analysis of nuclear dynamics.

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STREAMLINE

OHIO is one of six universities joining with three national laboratories on a new collaboration called STREAMLINE (SmarT Reduction and Emulation Applying Machine Learning in Nuclear Environments), recently announced as part of the to develop machine learning and AI methods to accelerate scientific discovery in nuclear physics research.

The STREAMLINE collaboration brings together a team of leading researchers in applying artificial intelligence and machine learning to theoretical nuclear physics. Their work begins where nuclear theory and super-computing intersect—large-scale computations that can dramatically increase scientists' power to understand nuclear structure and predict dynamic phenomena.

"We're going to be looking at ways to solve some of the most challenging problems in computational nuclear many-body theory," said Drischler, assistant professor of physics and astronomy in the College of Arts and Science at ÃÛèÖÊÓƵ and a Bridge Faculty member of the Facility for Rare Isotope Beams (FRIB) .

The "many-body problem" is central to the theory of quantum mechanics. The bodies are tiny particles, and the problem is that while the laws of physics effectively proscribe the properties of individual particles in the nucleus, the quantum system gets exponentially more complex as a collection of particles interact or "entangle."

In addition to OHIO, faculty in the project hail from Michigan State University, Florida State University, North Carolina State University, Ohio State University, and the University of Tennessee. The national labs involved include Argonne National Laboratory, Fermilab, and Oak Ridge National Laboratory.

The STREAMLINE team members are working to develop fast and accurate emulators, smart model extrapolation, and predictive models for nuclear dynamics, including nuclear fission and heavy-ion fusion. The group also plans an extensive program to educate the next generation of nuclear theorists.

"Our machine learning studies of correlated nucle