Artificial intelligence has helped uncover new laws of physics in plasma
Physicists at Emory University have used artificial intelligence to uncover new patterns in complex dust plasma systems—one of the states of matter.
Using a specially designed neural network, the researchers were able to describe so-called non-reciprocal forces—phenomena where the interaction between particles is not symmetric—with over 99% accuracy. This discovery could change the approach to studying complex physical systems.
The study, published in the journal PNAS, demonstrates that AI is capable not only of predicting events but also of serving as a tool for discovering fundamental physical principles.
The object of study was dust plasma—ionized gas containing charged microparticles, which is found in Saturn’s rings, Earth’s ionosphere, and during wildfires.
In the lab, researchers tracked the three-dimensional motion of dozens of particles in a vacuum chamber. The AI model, which was trained on a limited dataset, broke down the motion into three factors: environmental resistance, external forces (such as gravity), and complex interatomic interactions.
The results cast doubt on old theories. In particular, it turned out that the dependence of electric charge on particle size is much more complex than previously thought, and the rate at which interaction forces decay directly depends on the size of the objects.
Professor Justin Burton emphasized that this method is universal: it can be applied to the study of industrial paints, the dynamics of living cells, or even tumor metastasis. The authors note that despite the power of AI, a scientist’s critical thinking remains key to interpreting these discoveries in the “fourth state of matter.”
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