Physicists have used a machine learning approach to reveal unexpected details about how particles interact in complex systems. Their work focuses on non-reciprocal forces, where one particle influences another differently than it is influenced in return.
The findings, published in PNAS, come from a collaboration between experimental and theoretical physicists at Emory University. By combining a custom neural network with laboratory data from a dusty plasma, the team showed that artificial intelligence can do more than analyze data or make predictions. It can help uncover entirely new physical laws.
"We showed that we can use 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 new routes to discovery."
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