Tyler O'Neal, Staff Editor PHYSICS

UW physics prof discovers lasers trigger magnetism in atomically thin quantum materials

Researchers have discovered that light, in the form of a laser, can trigger a form of magnetism in a normally nonmagnetic material. This magnetism centers on the behavior of electrons. These subatomic particles have an electronic property called “spin,” which has a potential application in quantum supercomputing. The researchers found that electrons within the material became oriented in the same direction when illuminated by photons from a laser.

The experiment was led by scientists at the University of Washington and the University of Hong Kong. A cartoon depiction of the light-induced ferromagnetism that the researchers observed in ultrathin sheets of tungsten diselenide and tungsten disulfide. Laser light, shown in yellow, excites an exciton – a bound pair of an electron (blue) and its associated positive charge, also known as a hole (red). This activity induces long range exchange interactions among other holes trapped within the moiré superlattice, orienting their spins in the same direction. CREDIT Xi Wang/University of Washington

By controlling and aligning electron spins at this level of detail and accuracy, this platform could have applications in the field of quantum simulation, according to co-senior author Xiaodong Xu, a Boeing Distinguished Professor at the UW in the Department of Physics and the Department of Materials Science and Engineering.

“In this system, we can use photons essentially to control the ‘ground state’ properties — such as magnetism — of charges trapped within the semiconductor material,” said Xu, who is also a faculty researcher with the UW’s Clean Energy Institute and the Molecular Engineering & Sciences Institute. “This is a necessary level of control for developing certain types of qubits — or ‘quantum bits’ — for quantum computing and other applications.”

Xu, whose research team spearheaded the experiments, led the study with co-senior author Wang Yao, professor of physics at the University of Hong Kong, whose team worked on the theory underpinning the results. Other UW faculty members involved in this study are co-authors Di Xiao, a UW professor of physics and materials science and engineering who also holds a joint appointment at the Pacific Northwest National Laboratory, and Daniel Gamelin, a UW professor of chemistry and director of the Molecular Engineering Materials Center.

The team worked with ultrathin sheets — each just three layers of atoms thick — of tungsten diselenide and tungsten disulfide. Both are semiconductor materials, so named because electrons move through them at a rate between that of a fully conducting metal and an insulator, with potential uses in photonics and solar cells. Researchers stacked the two sheets to form a “moiré superlattice,” a stacked structure made up of repeating units.

Stacked sheets like these are powerful platforms for quantum physics and materials research because the superlattice structure can hold excitons in place. Excitons are bound pairs of “excited” electrons and their associated positive charges, and scientists can measure how their properties and behavior change in different superlattice configurations.

The researchers were studying the exciton properties within the material when they made the surprising discovery that light triggers a key magnetic property within the normally nonmagnetic material. Photons provided by the laser “excited” excitons within the laser beam’s path, and these excitons induced a type of long-range correlation among other electrons, with their spins all orienting in the same direction.

“It’s as if the excitons within the superlattice had started to ‘talk’ to spatially separated electrons,” said Xu. “Then, via excitons, the electrons established exchange interactions, forming what’s known as an ‘ordered state’ with aligned spins.”  

The spin alignment that the researchers witnessed within the superlattice is a characteristic of ferromagnetism, the form of magnetism intrinsic to materials like iron. It is normally absent from tungsten diselenide and tungsten disulfide. Each repeating unit within the moiré superlattice is essentially acting as a quantum dot to “trap” an electron spin, said Xu. Trapped electron spins that can “talk” to each other, as these can, have been suggested as the basis for a type of qubit, the basic unit for quantum computers that could harness the unique properties of quantum mechanics for computation.

In a separate paper published Nov. 25 in Science, Xu and his collaborators found new magnetic properties in moiré superlattices formed by ultrathin sheets of chromium triiodide. Unlike the tungsten diselenide and tungsten disulfide, chromium triiodide harbors intrinsic magnetic properties, even as a single atomic sheet. Stacked chromium triiodide layers formed alternating magnetic domains: ferromagnetic one — with spins all aligned in the same direction — and another that is “antiferromagnetic,” where spins point in opposite directions between adjacent layers of the superlattice and essentially “cancel each other out,” according to Xu. That discovery also illuminates relationships between a material’s structure and its magnetism that could propel future advances in supercomputing, data storage, and other fields.  

“It shows you the magnetic ‘surprises’ that can be hiding within moiré superlattices formed by 2D quantum materials,” said Xu. “You can never be sure what you’ll find unless you look.”

Barton’s lab develops new method that applies physics to models in epidemiology

Tyler O'Neal, Staff Editor PHYSICS

During the SARS-CoV-2 pandemic, multiple new and more transmissible variants of the virus have emerged. Understanding how specific mutations affect SARS-CoV-2 transmission could help us to better understand the biology of the virus and to control outbreaks. 

This, however, is a challenging task, said John Barton, an assistant professor of physics and astronomy at the University of California, Riverside, who is presenting results from his research titled ‘Inferring the Effects of Mutations on SARS-CoV-2 Transmission From Genomic Surveillance Data’ at the American Physical Society’s March Meeting. 

“Existing computational methods to study this problem tend to either be difficult to apply to large amounts of data or rely on very restrictive assumptions,” Barton said. “Experiments can also provide excellent information about how different mutations affect the virus, but they can’t be used to directly study SARS-CoV-2 transmission in humans.”

Barton and his colleagues developed a new computational method to solve this problem by applying techniques from statistical physics mathematical models in epidemiology. Their method allows them to look at genomic surveillance data — SARS-CoV-2 sequences collected from infected individuals — over time and across many regions throughout the world, and to find the effects of different mutations on SARS-CoV-2 transmission that best explain the observed evolutionary history of the virus throughout the pandemic. 

“A few novel features of our method are that it can account for the travel of infected individuals between regions, which most other models are unable to do, and that the physics-based methods that we use allow us to write down an exact mathematical expression for the transmission effects of different mutations, rather than relying on numerical simulations to estimate these parameters,” Barton said.

After validating their method on simulations, Barton and his colleagues applied it to more than 1.6 million SARS-CoV-2 sequences from the GISAID database, which were collected from 87 geographical regions. 

“Much research has focused on mutations in the Spike protein of SARS-CoV-2, and our analysis supports this emphasis on Spike as a main driver of SARS-CoV-2 transmission,” Barton said. “About half of the most impactful mutations that we find are in Spike, including three of the top four mutations. However, we also find multiple mutations outside of Spike that appears to strongly increase the transmission of the virus. Some of these may make good targets for future experiments to understand how different mutations affect SARS-CoV-2 function.”

Barton explained that their method is also sensitive enough to reveal benefits to SARS-CoV-2 transmission for mutations that were previously assumed to be neutral. His team is also able to detect some increased transmission for major new variants such as Alpha and Delta very rapidly, within a week of their appearance in regional data. The data set the team considered when writing the paper did not include sequences from the Omicron variant because the data was only collected up until August of 2021. 

“However, even without observing any Omicron sequences in the data, we would already estimate that Omicron would transmit more readily than Alpha just based on the mutations that it shares with other SARS-CoV-2 variants,” Barton said. “While we have focused specifically on SARS-CoV-2 in our analysis, our method is very general and could be applied to study the transmission of other pathogens, such as influenza.”

This research was led by graduate students Brian Lee and Elizabeth Finney in Barton’s lab, joined by collaborators Muhammad Sohail, Syed Ahmed, and Ahmed Quadeer at the Hong Kong University of Science and Technology; and Matthew McKay at the University of Melbourne, Australia. 

MIT student Mathews streamlines turbulence theory by combining ML, physics to model complex plasma phenomena

Tyler O'Neal, Staff Editor PHYSICS

To make fusion energy a viable resource for the world’s energy grid, researchers need to understand the turbulent motion of plasmas: a mix of ions and electrons swirling around in reactor vessels. The plasma particles, following magnetic field lines in toroidal chambers known as tokamaks, must be confined long enough for fusion devices to produce significant gains in net energy, a challenge when the hot edge of the plasma (over 1 million degrees Celsius) is just centimeters away from the much cooler solid walls of the vessel.

Abhilash Mathews, a Ph.D. candidate in the Department of Nuclear Science and Engineering working at MIT’s Plasma Science and Fusion Center (PSFC), believes this plasma edge to be a particularly rich source of unanswered questions. A turbulent boundary is central to understanding plasma confinement, fueling, and the potentially damaging heat fluxes that can strike material surfaces — factors that impact fusion reactor designs. Visualized are two-dimensional pressure fluctuations within a larger three-dimensional magnetically confined fusion plasma simulation. With recent advances in machine-learning techniques, these types of partial observations provide new ways to test reduced turbulence models in both theory and experiment. Credits:Image courtesy of the Plasma Science and Fusion Center.

To better understand edge conditions, scientists focus on modeling turbulence at this boundary using numerical simulations that will help predict the plasma's behavior. However, “first principles” simulations of this region are among the most challenging and time-consuming computations in fusion research. Progress could be accelerated if researchers could develop “reduced” supercomputer models that run much faster, but with quantified levels of accuracy.

For decades, tokamak physicists have regularly used a reduced “two-fluid theory” rather than higher-fidelity models to simulate boundary plasmas in experiments, despite uncertainty about accuracy. In a pair of recent publications, Mathews begins directly testing the accuracy of this reduced plasma turbulence model in a new way: he combines physics with machine learning.

“A successful theory is supposed to predict what you're going to observe,” explains Mathews, “for example, the temperature, the density, the electric potential, the flows. And it’s the relationships between these variables that fundamentally define a turbulence theory. What our work essentially examines is the dynamic relationship between two of these variables: the turbulent electric field and the electron pressure.”

Mathews employs a novel deep-learning technique that uses artificial neural networks to build representations of the equations governing the reduced fluid theory. With this framework, he demonstrates a way to compute the turbulent electric field from an electron pressure fluctuation in the plasma consistent with the reduced fluid theory. Models commonly used to relate the electric field to pressure break down when applied to turbulent plasmas, but this one is robust even too noisy pressure measurements.

Then, Mathews further investigates this connection, contrasting it against higher-fidelity turbulence simulations. This first-of-its-kind comparison of turbulence across models has previously been difficult — if not impossible — to evaluate precisely. Mathews finds that in plasmas relevant to existing fusion devices, the reduced fluid model's predicted turbulent fields are consistent with high-fidelity calculations. In this sense, the reduced turbulence theory works. But to fully validate it, “one should check every connection between every variable,” says Mathews.

Mathews’ advisor, Principal Research Scientist Jerry Hughes, notes that plasma turbulence is notoriously difficult to simulate, more so than the familiar turbulence seen in air and water. “This work shows that, under the right set of conditions, physics-informed machine-learning techniques can paint a very full picture of the rapidly fluctuating edge plasma, beginning from a limited set of observations. I’m excited to see how we can apply this to new experiments, in which we essentially never observe every quantity we want.”

These physics-informed deep-learning methods pave new ways in testing old theories and expanding what can be observed from new experiments. David Hatch, a research scientist at the Institute for Fusion Studies at the University of Texas at Austin, believes these applications are the start of a promising new technique.

“Abhi’s work is a major achievement with the potential for broad application,” he says. “For example, given limited diagnostic measurements of a specific plasma quantity, physics-informed machine learning could infer additional plasma quantities in a nearby domain, thereby augmenting the information provided by a given diagnostic. The technique also opens new strategies for model validation.”

Mathews sees exciting research ahead.

“Translating these techniques into fusion experiments for real edge plasmas is one goal we have insight into, and work is currently underway,” he says. “But this is just the beginning.”