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."

>High-Precision Insights Into Dusty Plasma Forces>

The study offers one of the most detailed descriptions to date of the physics governing dusty plasma. This system consists of ionized gas filled with interacting charged particles, including tiny grains of dust.

Using their AI model, the researchers were able to describe non-reciprocal forces with more than 99% accuracy. These forces are notoriously difficult to measure and model.

"We can describe these forces with an accuracy of more than 99%," says Ilya Nemenman, an Emory professor of theoretical physics and co-senior author of the paper. "What's even more interesting is that we show that some common theoretical assumptions about these forces are not quite accurate. We're able to correct these inaccuracies because we can now see what's occurring in such exquisite detail."

The team believes this method could be applied broadly to systems made up of many interacting components. These range from industrial materials such as paint and ink to groups of living cells.