Physicists used a machine-learning method to identify surprising new twists on the non-reciprocal forces governing a many-body system.
The journal PNAS published the findings by experimental and theoretical physicists at Emory University, based on a neural network model and data from laboratory experiments on dusty plasma — ionized gas containing suspended dust particles.
The work is one of the relatively few instances of using AI not as a data processing or predictive tool, but to discover new physical laws governing the natural world.
"We showed that we can us AI to discover new physics," says Justin Burton, an Emory professor of experimental physics and senior co-author of the paper. "Our AI method is not a black box: we understand how and why it works. The framework it provides is also universal. It could potentially be applied to other many-body systems to open to new routes to discovery."
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