Physicists have achieved significant success in uncovering new natural laws by applying artificial intelligence (AI). A research team at Emory University combined specially designed neural networks with precise three-dimensional tracking of particles within dusty plasma, revealing hidden patterns in complex particle interactions. Plasma, considered the fourth state of matter, is a mysterious phase found from outer space to Earth’s wildfires. The most intriguing aspect of this study is that AI did not merely analyze data but challenged long-established physics principles to discover new truths.
Plasma is a state in which gas is ionized so that electrons and ions move freely, exhibiting unique properties such as electrical conductivity. Approximately 99.9 percent of the visible universe consists of plasma. Dusty plasma includes, along with ions, small dust particles and extends from Saturn’s rings to Earth’s ionosphere. Researchers recorded the motion of these particles with extreme precision in a vacuum chamber using lasers and high-speed cameras. When feeding the collected data into an AI model, it successfully explained the non-reciprocal forces—that is, forces that are not mutual—between particles with over 99 percent accuracy.
The concept of non-reciprocal forces is highly complex. It describes situations where the influence one particle exerts on another differs from the response back. The research team illustrated this with the example of two boats moving in rhythm, where the wake from the front boat pulls the rear one forward, while the rear boat pushes the front one back. In dusty plasma, a similar unusual behavior was observed: the leading particle attracts the one behind it, but the trailing particle consistently repels the one ahead. Earlier theories assumed a simple relationship between particle electrical charge and size, but AI clarified that this relationship is a complex process dependent on plasma density and temperature.
According to the scientists, this AI method is not a “black box” but was designed for easy human interpretation. It examines particle motion by dividing it into three main influences: drag caused by velocity, environmental forces like gravity, and mutual forces between particles. This technology shows potential beyond physics, extending into bioengineering and biology. For instance, it could open new avenues for understanding how cancer cells spread, or how flocking behavior in birds and collective human dynamics develop. This research positions AI not just as a tool but as a partner in discovering new laws of nature.
