AI Reveals Unexpected New Physics in the Fourth State of Matter

A new theoretical approach aims to shed light on the complex behavior of many-body systems.

Researchers have applied a machine learning technique to uncover unexpected features of the non-reciprocal forces that shape the behavior of a many-body system.

The study, published in PNAS, was conducted by experimental and theoretical physicists at Emory University. It combines a specially designed neural network with laboratory measurements from a dusty plasma, a type of ionized gas that contains interacting particles. Unlike most uses of artificial intelligence in science, which focus on analyzing data or making predictions, this work used AI to help reveal previously unknown 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.”

According to the team, the paper delivers the most detailed account so far of the physics governing dusty plasmas, including highly accurate descriptions of non-reciprocal forces.

“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 researchers believe their AI-based strategy could be used to infer physical laws in many other systems made up of large numbers of interacting particles. These include colloids such as paint and ink, as well as clusters of cells in living organisms.

Wentao Yu, now a postdoctoral fellow at the California Institute of Technology, is the paper’s first author and carried out the research as an Emory PhD student. Co-author Eslam Abdelaleem also worked on the project as an Emory graduate student and is now a postdoctoral fellow at Georgia Tech.

Funding for the project came primarily from the National Science Foundation, with additional support from the Simons Foundation.

“This project serves as a great example of an interdisciplinary collaboration where the development of new knowledge in plasma physics and AI may lead to further advances in the study of living systems,” says Vyacheslav (Slava) Lukin, program director for the NSF Plasma Physics program. “The dynamics of these complex systems is dominated by collective interactions that emerging AI techniques may help us to better describe, recognize, understand, and even control.”

The fourth state of matter

Plasma is often called the fourth state of matter. It consists of ionized gas in which electrons and ions move freely, giving it distinctive properties such as electrical conductivity. Plasma is believed to make up about 99.9% of the visible universe, appearing in phenomena that range from solar wind streaming from the Sun’s corona to lightning striking Earth.

Dusty plasma contains not only ions and electrons but also charged dust particles. It appears in many cosmic and planetary settings, including Saturn’s rings and Earth’s ionosphere.

On the Moon, weak gravity allows charged dust particles to hover above the surface, creating a natural example of dusty plasma. “That’s why when astronauts walk on the moon their suits get covered in dust,” Burton explains.

Dusty plasma can also form on Earth. During wildfires, soot particles mix with smoke and can become electrically charged. These charged particles may disrupt radio communications used by firefighters.

Burton’s research focuses on dusty plasmas and amorphous materials. In his laboratory, tiny plastic particles are suspended in a plasma-filled vacuum chamber to serve as a simplified model of more complicated systems. By adjusting the gas pressure inside the chamber, researchers can reproduce different physical conditions and observe how the system responds to external forces.

For this study, Burton and Yu created a tomographic imaging method to monitor the three-dimensional (3D) motion of particles in a dusty plasma. A laser sheet scans through the chamber while a high-speed camera records images. By stacking images taken at different positions, the team reconstructed the 3D locations of dozens of particles over centimeter-scale distances for several minutes.

Understanding collective motion

Nemenman, a theoretical biophysicist, studies the principles that govern dynamic natural systems, particularly complex biological ones. He is especially interested in collective motion, such as how cells move and coordinate within the human body.

“General questions of how a whole system arises from interactions of tiny parts are very important,” Nemenman explains. “In cancer, for instance, you want to understand how the interaction of cells may relate to some of them breaking away from a tumor and moving to a new place, becoming metastatic.”

Although he often collaborates with life scientists, Nemenman saw the dusty plasma project as a chance to explore collective behavior in a simpler setting than a living organism. That made it a useful test case for applying AI to uncover new physical insights.

“For all the talk about how AI is revolutionizing science, there are very few examples where something fundamentally new has been found directly by an AI system,” Nemenman says.

Designing a neural network

One of the most famous examples of AI, ChatGPT, trains on the vast amount of information available on the internet in order to predict the appropriate text in response to a prompt.

“When you’re probing something new, you don’t have a lot of data to train AI,” Nemenman explains. “That meant we would have to design a neural network that could be trained with a small amount of data and still learn something new.”

Burton, Nemenman, Yu, and Abdelaleem met weekly in a conference room to discuss the problem.

“We needed to structure the network to follow the necessary rules while still allowing it to explore and infer unknown physics,” Burton explains.

“It took us more than a year of back-and-forth discussions in these weekly meetings,” Nemenman adds. “Once we came up with the correct structure of the network to train, it turned out to be fairly simple.”

The physicists distilled the restraints for the neural network to modeling three independent contributions to particle motion: the effect of velocity, or drag force; the environmental forces, such as gravity; and the particle-to-particle forces.

The surprising results

Trained on 3D particle trajectories, the AI model accounted for inherent symmetries, non-identical particles and learned the effective non-reciprocal forces between particles with exquisite accuracy.

To explain these non-reciprocal forces, the researchers use the analogy of two boats moving across a lake, creating waves. The wake pattern of each boat affects the motion of the other boat. The wake of one boat may repel or attract the other boat depending on their relative positions. For example, whether the boats are traveling side by side or one behind the other.

“In a dusty plasma, we described how a leading particle attracts the trailing particle, but the trailing particle always repels the leading one,” Nemenman explains. “This phenomenon was expected by some but now we have a precise approximation for it which didn’t exist previously.”

Their findings also correct some wrong assumptions about dusty plasma.

For example, a longstanding theory held that the larger the radius of a dust particle, the larger the charge that stuck to that particle, in exact proportion to the radius of the particle. “We showed that this theory is not quite right,” Nemenman says. “While it’s true that the larger the particle the larger the charge, that increase is not necessarily proportional to the radius. It depends on the density and temperature of the plasma.”

Another theory held that the forces between two particles falls off exponentially in direct relationship to the distance between two particles and that the factor by which it drops is not dependent on the size of the particle. The new AI method showed that the drop off in force does depend on the particle size.

The researchers verified their findings through experiments.

A universal framework

Their physics-based neural network runs on a desktop computer and offers a universal, theoretical framework to unravel mysteries about other complex, many-body systems.

Nemenman, for example, is looking forward to an upcoming visiting professorship at the Konstanz School of Collective Behavior in Germany. The school brings together interdisciplinary approaches to study the burgeoning field of collective behavior, everything from flocking birds to schools of fish and human crowds.

“I’ll be teaching students from all over the world how to use AI to infer the physics of collective motion — not within a dusty plasma but within a living system,” he says.

While their AI framework holds the ability to infer new physics, expert human physicists are needed to design the right structure for the neural network, to interpret, and to validate the resulting data.

“It takes critical thinking to develop and use AI tools in ways that make real advances in science, technology, and the humanities,” Burton says.

He feels optimistic about the potential for AI to benefit society.

“I think of it like the Star Trek motto, to boldly go where no one has before,” Burton says. “Used properly, AI can open doors to whole new realms to explore.”

Reference: “Physics-tailored machine learning reveals unexpected physics in dusty plasmas” by Wentao Yu, Eslam Abdelaleem, Ilya Nemenman and Justin C. Burton, 31 July 2025, Proceedings of the National Academy of Sciences.
DOI: 10.1073/pnas.2505725122

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