Computers Math

Every day, the sun ejects large amounts of a hot particle soup known as plasma toward Earth where it can disrupt telecommunications satellites and damage electrical grids. Now, scientists at the U.S. Department of Energy’s (DOE) Princeton Plasma Physics Laboratory (PPPL) and Princeton University’s Department of Astrophysical Sciences have made a discovery that could lead to better predictions of this space weather and help safeguard sensitive infrastructure.
The discovery comes from a new computer model that predicts the behavior of the plasma in the region above the surface of the sun known as the solar corona. The model was originally inspired by a similar model that describes the behavior of the plasma that fuels fusion reactions in doughnut-shaped fusion facilities known as tokamaks.
Fusion, the power that drives the sun and stars, combines light elements in the form of plasma — the hot, charged state of matter composed of free electrons and atomic nuclei — that generates massive amounts of energy. Scientists are seeking to replicate fusion on Earth for a virtually inexhaustible supply of power to generate electricity.
The Princeton scientists made their findings while studying roped-together magnetic fields that loop into and out of the sun. Under certain conditions, the loops can cause hot particles to erupt from the sun’s surface in enormous burps known as coronal mass ejections. Those particles can eventually hit the magnetic field surrounding Earth and cause auroras, as well as interfere with electrical and communications systems.
“We need to understand the causes of these eruptions to predict space weather,” said Andrew Alt, a graduate student in the Princeton Program in Plasma Physics at PPPL and lead author of the paper reporting the results in the Astrophysical Journal.
The model relies on a new mathematical method that incorporates a novel insight that Alt and collaborators discovered into what causes the instability. The scientists found that a type of jiggling known as the “torus instability” could cause roped magnetic fields to untether from the sun’s surface, triggering a flood of plasma.
The torus instability loosens some of the forces keeping the ropes tied down. Once those forces weaken, another force causes the ropes to expand and lift further off the solar surface. “Our model’s ability to accurately predict the behavior of magnetic ropes indicates that our method could ultimately be used to improve space weather prediction,” Alt said.
The scientists have also developed a way to more accurately translate laboratory results to conditions on the sun. Past models have relied on assumptions that made calculations easier but did not always simulate plasma precisely. The new technique relies only on raw data. “The assumptions built into previous models remove important physical effects that we want to consider,” Alt said. “Without these assumptions, we can make more accurate predictions.”
To conduct their research, the scientists created magnetic flux ropes inside PPPL’s Magnetic Reconnection Experiment (MRX), a barrel-shaped machine designed to study the coming together and explosive breaking apart of the magnetic field lines in plasma. But flux ropes created in the lab behave differently than ropes on the sun, since, for example, the flux ropes in the lab have to be contained by a metal vessel.
The researchers made alterations to their mathematical tools to account for these differences, ensuring that results from MRX could be translated to the sun. “There are conditions on the sun that we cannot mimic in the laboratory,” said PPPL physicist Hantao Ji, a Princeton University professor who advises Alt and contributed to the research. “So, we adjust our equations to account for the absence or presence of certain physical properties. We have to make sure our research compares apples to apples so our results will be accurate.”
Discovery of the jiggling plasma behavior could also lead to more efficient generation of fusion-powered electricity. Magnetic reconnection and related plasma behavior occur in tokamaks as well as on the sun, so any insight into these processes could help scientists control them in the future.
Support for this research came from the DOE, the National Aeronautics and Space Administration, and the German Research Foundation. Research partners include Princeton University, Sandia National Laboratories, the University of Potsdam, the Harvard-Smithsonian Center for Astrophysics, and the Bulgarian Academy of Sciences.
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Materials provided by DOE/Princeton Plasma Physics Laboratory. Original written by Raphael Rosen. Note: Content may be edited for style and length. More

Scientists characterized how the electronic states in a compound containing iron, tellurium, and selenium depend on local chemical concentrations. They discovered that superconductivity (conducting electricity without resistance), along with distinct magnetic correlations, appears when the local concentration of iron is sufficiently low; a coexisting electronic state existing only at the surface (topological surface state) arises when the concentration of tellurium is sufficiently high. Reported in Nature Materials, their findings point to the composition range necessary for topological superconductivity. Topological superconductivity could enable more robust quantum computing, which promises to deliver exponential increases in processing power.
“Quantum computing is still in its infancy, and one of the key challenges is reducing the error rate of the computations,” said first author Yangmu Li, a postdoc in the Neutron Scattering Group of the Condensed Matter Physics and Materials Science (CMPMS) Division at the U.S. Department of Energy’s (DOE) Brookhaven National Laboratory. “Errors arise as qubits, or quantum information bits, interact with their environment. However, unlike trapped ions or solid-state qubits such as point defects in diamond, topological superconducting qubits are intrinsically protected from part of the noise. Therefore, they could support computation less prone to errors. The question is, where can we find topological superconductivity?
In this study, the scientists narrowed the search in one compound known to host topological surface states and part of the family of iron-based superconductors. In this compound, topological and superconducting states are not distributed uniformly across the surface. Understanding what’s behind these variations in electronic states and how to control them is key to enabling practical applications like topologically protected quantum computing.
From previous research, the team knew modifying the amount of iron could switch the material from a superconducting to nonsuperconducting state. For this study, physicist Gendu Gu of the CMPMS Division grew two types of large single crystals, one with slightly more iron relative to the other. The sample with the higher iron content is nonsuperconducting; the other sample is superconducting.
To understand whether the arrangement of electrons in the bulk of the material varied between the superconducting and nonsuperconducting samples, the team turned to spin-polarized neutron scattering. The Spallation Neutron Source (SNS), located at DOE’s Oak Ridge National Laboratory, is home to a one-of-a-kind instrument for performing this technique.
“Neutron scattering can tell us the magnetic moments, or spins, of electrons and the atomic structure of a material,” explained corresponding author, Igor Zaliznyak, a physicist in the CMPMS Division Neutron Scattering Group who led the Brookhaven team that helped design and install the instrument with collaborators at Oak Ridge. “In order to single out the magnetic properties of electrons, we polarize the neutrons using a mirror that reflects only one specific spin direction.”
To their surprise, the scientists observed drastically different patterns of electron magnetic moments in the two samples. Therefore, the slight alteration in the amount of iron caused a change in electronic state. More

Using naturalistic driving data and machine learning techniques, researchers at Columbia University Mailman School of Public Health and Columbia’s Fu Foundation School of Engineering and Applied Science have developed highly accurate algorithms for detecting mild cognitive impairment and dementia in older drivers. Naturalistic driving data refer to data captured through in-vehicle recording devices or other technologies in the real-world setting. These data could be processed to measure driving exposure, space and performance in great detail. The findings are published in the journal Geriatrics.
The researchers developed random forests models, a statistical technique widely used in AI for classifying disease status, that performed exceptionally well. “Based on variables derived from the naturalistic driving data and basic demographic characteristics, such as age, sex, race/ethnicity and education level, we could predict mild cognitive impairment and dementia with 88 percent accuracy, “said Sharon Di, associate professor of civil engineering and engineering mechanics at Columbia Engineering and the study’s lead author.
The investigators constructed 29 variables using the naturalistic driving data captured by in-vehicle recording devices from 2977 participants of the Longitudinal Research on Aging Drivers (LongROAD) project, a multisite cohort study sponsored by the AAA Foundation for Traffic Safety. At the time of enrollment, the participants were active drivers aged 65-79 years and had no significant cognitive impairment and degenerative medical conditions. Data used in this study spanned the time period from August 2015 through March 2019.
Among the 2977 participants whose cars were instrumented with the in-vehicle recording devices, 33 were newly diagnosed with mild cognitive impairment and 31 with dementia by April 2019. The researchers trained a series of machine learning models for detecting mild cognitive impairment/dementia and found that the model based on driving variables and demographic characteristics was 88 percent accurate, much better than models based on demographic characteristics only (29 percent) and driving variables only (66 percent). Further analysis revealed that age was most predictive of mild cognitive impairment and dementia, followed by the percentage of trips traveled within 15 miles of home, race/ethnicity, minutes per trip chain (i.e., length of trips starting and ending at home), minutes per trip, and number of hard braking events with deceleration rates ≥ 0.35 g.
“Driving is a complex task involving dynamic cognitive processes and requiring essential cognitive functions and perceptual motor skills. Our study indicates that naturalistic driving behaviors can be used as comprehensive and reliable markers for mild cognitive impairment and dementia,” said Guohua Li, MD, DrPH, professor of epidemiology and anesthesiology at Columbia Mailman School of Public Health and Vagelos College of Physicians and Surgeons, and senior author. “If validated, the algorithms developed in this study could provide a novel, unobtrusive screening tool for early detection and management of mild cognitive impairment and dementia in older drivers.”
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Materials provided by Columbia University’s Mailman School of Public Health. Note: Content may be edited for style and length. More

Researchers at the University of Bath investigating how virtual reality (VR) can help improve balance believe this technology could be a valuable tool in the prevention of falls.
As people grow older, losing balance and falling becomes more common, which increases the risk of injury and affects the person’s independence.
Falls are the leading cause of non-fatal injuries in over 65-yearolds and account for over 4 million bed days per year in England alone, at an estimated cost of £2 billion.
Humans use three ways of keeping their balance: vision, proprioceptive (physical feedback from muscles and joints) and vestibular system (feedback from semi-circular canals in the ear). Of these, vision is the most important.
Traditional ways of assessing balance include patient surveys and physical tests such as using a treadmill or testing agility when performing specific movements or exercises.
However, the accuracy of these tests can be affected by age, sex and motivation, and the movements measured aren’t necessarily reflective of real-life scenarios. More

Researchers have big ideas for the potential of quantum technology, from unhackable networks to earthquake sensors. But all these things depend on a major technological feat: being able to build and control systems of quantum particles, which are among the smallest objects in the universe.
That goal is now a step closer with the publication of a new method by University of Chicago scientists. Published April 28 in Nature, the paper shows how to bring multiple molecules at once into a single quantum state — one of the most important goals in quantum physics.
“People have been trying to do this for decades, so we’re very excited,” said senior author Cheng Chin, a professor of physics at UChicago who said he has wanted to achieve this goal since he was a graduate student in the 1990s. “I hope this can open new fields in many-body quantum chemistry. There’s evidence that there are a lot of discoveries waiting out there.”
One of the essential states of matter is called a Bose-Einstein condensate: When a group of particles cooled to nearly absolute zero share a quantum state, the entire group starts behaving as though it were a single atom. It’s a bit like coaxing an entire band to march entirely in step while playing in tune — difficult to achieve, but when it happens, a whole new world of possibilities can open up.
Scientists have been able to do this with atoms for a few decades, but what they’d really like to do is to be able to do it with molecules. Such a breakthrough could serve as the underpinning for many forms of quantum technology.
But because molecules are larger than atoms and have many more moving parts, most attempts to harness them have dissolved into chaos. “Atoms are simple spherical objects, whereas molecules can vibrate, rotate, carry small magnets,” said Chin. “Because molecules can do so many different things, it makes them more useful, and at the same time much harder to control.”
Chin’s group wanted to take advantage of a few new capabilities in the lab that had recently become available. Last year, they began experimenting with adding two conditions. More

In the quantum realm, electrons can group together to behave in interesting ways. Magnetism is one of these behaviors that we see in our day-to-day life, as is the rarer phenomena of superconductivity. Intriguingly, these two behaviors are often antagonists, meaning that the existence of one of them often destroys the other. However, if these two opposite quantum states are forced to coexist artificially, an elusive state called a topological superconductor appears, which is exciting for researchers trying to make topological qubits.
Topological qubits are exciting as one of the potential technologies for future quantum computers. In particular, topological qubits provide the basis for topological quantum computing, which is attractive because it is much less sensitive to interference from its surroundings from perturbing the measurements. However, designing and controlling topological qubits has remained a critically open problem, ultimately due to the difficulty of finding materials capable of hosting these states, such as topological superconductors.
To overcome the elusiveness of topological superconductors, which are remarkably hard to find in natural materials, physicists have developed methodologies to engineer these states by combining common materials. The basic ingredients to engineer topological superconductors — magnetism and superconductivity — often require combining dramatically different materials. What’s more, creating a topological superconducting material requires being able to finely tune the magnetism and superconductivity, so researchers have to prove that their material can be both magnetic and superconductive at the same time, and that they can control both properties. In their search for such a material, researchers have turned to graphene.
Graphene — a single layer of carbon atoms — represents a highly controllable and common material and has been raised as one of the critical materials for quantum technologies. However, the coexistence of magnetism and superconductivity has remained elusive in graphene, despite long-standing experimental efforts that demonstrated the existence of these two states independently. This fundamental limitation represents a critical obstacle towards the development of artificial topological superconductivity in graphene.
In a recent breakthrough experiment, researchers at the UAM in Spain, CNRS in France, and INL in Portugal, together with the theoretical support of Prof. Jose Lado at Aalto University, have demonstrated an initial step along a pathway towards topological qubits in graphene. The researchers demonstrated that single layers of graphene can host simultaneous magnetism and superconductivity, by measuring quantum excitations unique to this interplay. This breakthrough finding was accomplished by combining the magnetism of crystal domains in graphene, and the superconductivity of deposited metallic islands.
‘This experiment shows that two key paradigmatic quantum orders, superconductivity, and magnetism, can simultaneously coexist in graphene,’ said Professor Jose Lado, ‘Ultimately, this experiment demonstrates that graphene can simultaneously host the necessary ingredients for topological superconductivity. While in the current experiment we have not yet observed topological superconductivity, building on top of this experiment we can potentially open a new pathway towards carbon-based topological qubits.’
The researchers induced superconductivity in graphene by depositing an island of a conventional superconductor close to grain boundaries, naturally forming seams in the graphene which have a slightly different magnetic properties to the rest of the material. The superconductivity and grain boundary magnetism was demonstrated to give rise to Yu-Shiba-Rusinov states, which can only exists in a material when magnetism and superconductivity coexisting together. The phenomena the team observed in the experiment matched up with the theoretical model developed by Professor Lado, showing that the researchers can fully control the quantum phenomena in their designer hybrid system.
The demonstration of Yu-Shiba-Rusinov states in graphene is the first step towards the ultimate development of graphene-based topological qubits. In particular, by carefully controlling Yu-Shiba-Rusinov states, topological superconductivity and Majorana states can be created. Topological qubits based on Majorana states can potentially drastically overcome the limitations of current qubits, protecting quantum information by exploiting the nature of these unconventional states. The emergence of these states requires meticulous control of the system parameters. The current experiment establishes the critical starting point towards this goal, which can be built upon to hopefully open a disruptive road to carbon-based topological quantum computers.
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Materials provided by Aalto University. Note: Content may be edited for style and length. More

Researchers at the University of California, Riverside, have used a nanoscale synthetic antiferromagnet to control the interaction between magnons — research that could lead to faster and more energy-efficient computers.
In ferromagnets, electron spins point in the same direction. To make future computer technologies faster and more energy-efficient, spintronics research employs spin dynamics — fluctuations of the electron spins — to process information. Magnons, the quantum-mechanical units of spin fluctuations, interact with each other, leading to nonlinear features of the spin dynamics. Such nonlinearities play a central role in magnetic memory, spin torque oscillators, and many other spintronic applications.
For example, in the emergent field of magnetic neuromorphic networks — a technology that mimics the brain — nonlinearities are essential for tuning the response of magnetic neurons. Also, in another frontier area of research, nonlinear spin dynamics may become instrumental.
“We anticipate the concepts of quantum information and spintronics to consolidate in hybrid quantum systems,” said Igor Barsukov, an assistant professor at the Department of Physics & Astronomy who led the study that appears in Applied Materials & Interfaces. “We will have to control nonlinear spin dynamics at the quantum level to achieve their functionality.”
Barsukov explained that in nanomagnets, which serve as building blocks for many spintronic technologies, magnons show quantized energy levels. Interaction between the magnons follows certain symmetry rules. The research team learned to engineer the magnon interaction and identified two approaches to achieve nonlinearity: breaking the symmetry of the nanomagnet’s spin configuration; and modifying the symmetry of the magnons. They chose the second approach.
“Modifying magnon symmetry is the more challenging but also more application-friendly approach,” said Arezoo Etesamirad, the first author of the research paper and a graduate student in Barsukov’s lab.
In their approach, the researchers subjected a nanomagnet to a magnetic field that showed nonuniformity at characteristic nanometer length scales. This nanoscale nonuniform magnetic field itself had to originate from another nanoscale object.
For a source of such a magnetic field, the researchers used a nanoscale synthetic antiferromagnet, or SAF, consisting of two ferromagnetic layers with antiparallel spin orientation. In its normal state, SAF generates nearly no stray field — the magnetic field surrounding the SAF, which is very small. Once it undergoes the so-called spin-flop transition, the spins become canted and the SAF generates a stray field with nonuniformity at nanoscale, as needed. The researchers switched the SAF between the normal state and the spin-flop state in a controlled manner to toggle the symmetry-breaking field on and off.
“We were able to manipulate the magnon interaction coefficient by at least one order of magnitude,” Etesamirad said. “This is a very promising result, which could be used to engineer coherent magnon coupling in quantum information systems, create distinct dissipative states in magnetic neuromorphic networks, and control large excitation regimes in spin-torque devices.”
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Materials provided by University of California – Riverside. Original written by Iqbal Pittalwala. Note: Content may be edited for style and length. More

Isaac Newton may have met his match.
For centuries, engineers have relied on physical laws — developed by Newton and others — to understand the stresses and strains on the materials they work with. But solving those equations can be a computational slog, especially for complex materials.
MIT researchers have developed a technique to quickly determine certain properties of a material, like stress and strain, based on an image of the material showing its internal structure. The approach could one day eliminate the need for arduous physics-based calculations, instead relying on computer vision and machine learning to generate estimates in real time.
The researchers say the advance could enable faster design prototyping and material inspections. “It’s a brand new approach,” says Zhenze Yang, adding that the algorithm “completes the whole process without any domain knowledge of physics.”
The research appears today in the journal Science Advances. Yang is the paper’s lead author and a PhD student in the Department of Materials Science and Engineering. Co-authors include former MIT postdoc Chi-Hua Yu and Markus Buehler, the McAfee Professor of Engineering and the director of the Laboratory for Atomistic and Molecular Mechanics.
Engineers spend lots of time solving equations. They help reveal a material’s internal forces, like stress and strain, which can cause that material to deform or break. Such calculations might suggest how a proposed bridge would hold up amid heavy traffic loads or high winds. Unlike Sir Isaac, engineers today don’t need pen and paper for the task. “Many generations of mathematicians and engineers have written down these equations and then figured out how to solve them on computers,” says Buehler. “But it’s still a tough problem. It’s very expensive — it can take days, weeks, or even months to run some simulations. So, we thought: Let’s teach an AI to do this problem for you.”
The researchers turned to a machine learning technique called a Generative Adversarial Neural Network. They trained the network with thousands of paired images — one depicting a material’s internal microstructure subject to mechanical forces, and the other depicting that same material’s color-coded stress and strain values. With these examples, the network uses principles of game theory to iteratively figure out the relationships between the geometry of a material and its resulting stresses. More