When Order Hides in Plain Sight

The 2021 Nobel Prize for Physics went to three scientists for their work on complexity. Syukuro Manabe and Klaus Hasselmann won, according to a prize announcement, “for the physical modelling of Earth’s climate, quantifying variability and reliably predicting global warming.” Giorgio Parisi won “for the discovery of the interplay of disorder and fluctuations in physical systems from atomic to planetary scales.” All three showed there is a simplicity to complexity and vice versa: The interactions of elementary parts can give rise to sophisticated behavior, and yet in such behavior we sometimes see universal large-scale patterns.

“Complexity science was long deserving of a Nobel Prize,” says Serim Ilday, a professor at UNAM (National Nanotechnology Research Center), Bilkent University, Turkey. “It shows complex systems’ importance and argues that they can be understood. Previously, people were saying that complexity was just a buzzword. But if you methodically approach these systems, then you can really understand how they behave.”

Ilday, who runs the Simply Complex Lab, has come up with a methodical approach for controlling and measuring systems that look messy but aren’t. Her work is largely experimental but could change the way we develop new materials, treat disease, or even—just maybe—look for life on other planets.

“Scientists often find complicated ways to explain simple phenomena,” Ilday says. She strips away excess, as Picasso did. “If you can understand the phenomenon with already-known theories, then you don’t need anything else.”

As an example, she points to her time as a graduate student. Scientists were stumped by the way a pile of nanoparticles transitions from a set of individual elements into a connected but confined nanoparticle network. She viewed them as a network, then reduced the dimensionality from three to two, and applied a well-understood framework from physics called percolation theory. “My adviser and colleagues were trying to understand it through a complicated chemical reaction mechanism,” she says. But the problems didn’t require chemistry—just basic physics. Now she’s applying physics to more types of problems.

The lab’s platform consists of more than the physical setup. The team also developed a computational toolbox, SCLAB. Their software first detects contrast in the video frames, then shapes, then the locations of particles. It calculates the distances between them, the packing density in different areas, particles’ velocities, and the overall orderliness according to different statistical measures.

They used MATLAB^® throughout the process to control the laser, collect images from the camera, and perform data analysis, including image processing, statistical calculations, and machine learning. As an example of how much control they had over the laser—and the complex systems under observation—they used the laser to arrange particles into a star, the word “Hi!”, and a rotating rectangle, as described in their Nature Physics paper. Their images, produced using MATLAB, made the cover of the issue.

“The graphical user interface in MATLAB is one of its strongest points,” says Michaël Barbier, a research scientist in the lab. Some parts of the pipeline exist only in other programming languages, but Makey says, “The compatibility between MATLAB and other languages is very helpful.”

The team is increasingly using machine learning in their work. One task is particle detection. At first, they would manually tag particles in images, then use that as training data for a neural network. But manual annotation took too long, and the network’s performance wasn’t much better than that of programmed heuristics.