Aurelia Butler

Computer scientists and mathematicians working in complex systems at the University of Sydney and the Max Planck Institute for Mathematics in the Sciences in Germany have developed new methods to describe what many of us take for granted — how easy, or hard, it can be to fall in and out of sync.
Synchronised phenomena are all around us, whether it is human clapping and dancing, or the way fireflies flash, or how our neurons and heart cells interact. However, it is something not fully understood in engineering and science.
Associate Professor Joseph Lizier, expert in complex systems at the University of Sydney, said: “We know the feeling of dancing in step to the ‘Nutbush’ in a crowd — or the awkward feeling when people lose time clapping to music. Similar processes occur in nature, and it is vital that we better understand how falling in and out of sync actually works.
“Being in sync in a system can be very good; you want your heart cells to all beat together rather than fibrillate. But being in sync can also be very bad; you don’t want your brain cells to all fire together in an epileptic seizure.”
Associate Professor Lizier and colleagues at the Max Planck Institute in Leipzig, Germany have published new research on synchronisation in the Proceedings of the National Academy of Sciences (PNAS).
The paper sets out the mathematics of how the network structure connecting a set of individual elements controls how well they can synchronise their activity. It is a critical insight into how these systems operate, because in most real-world systems, no one individual element controls all the others. And nor can any individual directly see and react to all the others: they are only connected through a network.
Associate Professor Lizier, from the Centre of Complex Systems and the School of Computer Science in the Faculty of Engineering, said: “Our results open new opportunities for designing network structures or interventions in networks. This could be super useful in stabilising electricity in power grids, vital for the transition to renewables, or to avoid neural synchronisation in the brain, which can trigger epilepsy.”
To understand how these systems work, the researchers studied what are known as “walks” through a network in a complex system. Walks are sequences of connected hops between individual elements or nodes in the network. More

Researchers at the Center for Functional Nanomaterials (CFN), a U.S. Department of Energy (DOE) Office of Science User Facility at DOE’s Brookhaven National Laboratory, and Northrop Grumman, a multinational aerospace and defense technology company, have found a way to maintain valley polarization at room temperature using novel materials and techniques. This discovery could lead to devices that store and process information in novel ways using this technology without the need to keep them at ultra-low temperatures. Their research was recently published in Nature Communications.
One of the paths being explored to achieve these devices is a relatively new field called “valleytronics.” A material’s electronic band structure — the range of energy levels in each atom’s electron configurations — can dip up or down. These peaks and troughs are known as “valleys.” Some materials have multiple valleys with the same energy. An electron in a system like this can occupy any one of these valleys, presenting a unique way to store and process information based on which valley the electron occupies. One challenge, however, has been the effort and expense of maintaining the low temperatures needed to keep valley polarization stable. Without this stability, devices would begin to lose information. In order to make a technology like this feasible for practical, affordable applications, experts would need to find a way to around this constraint.
Exploring 2D Landscapes for the Perfect Valleys
Transition metal dichalcogenides (TMDs) are interesting, layered materials that can be, at their thinnest, only few atoms thick. Each layer in the material consists of a two-dimensional (2D) sheet of transition metal atoms sandwiched between chalcogen atoms. While the metal and the chalcogen are strongly bound by covalent bonds in a layer, adjacent layers are only weakly bound by van der Waal’s interactions. The weak bonds that hold these layers together enable TMDs to be exfoliated down to a monolayer that’s only one “molecule” thick. These are often referred to as 2D materials.
The team at CFN synthesized single crystals of chiral lead halide perovskites (R/S-NEAPbI3). Chirality describes a set of objects, like molecules, that are a mirror image of each other but can’t be superimposed. It is derived from the Greek word for “hands,” a perfect example of chirality. The two shapes are identical, but if you put one hand on top of the other, they will not align. This asymmetry is important for controlling valley polarization.
Flakes of this material, roughly 500 nanometers thick or five-thousandths the thickness of a human hair, were layered onto a monolayer of molybdenum disulfide (MoS2) TMD to create what is known as a heterostructure. By combining different 2D materials with properties that affect the charge transfer at the interface between the two materials, these heterostructures open up a world of possibility.
After creating and characterizing this heterostructure, the team was eager to see how it behaved. More

An artificial intelligence tool developed by researchers at the University of Rochester can help people with Parkinson’s disease remotely assess the severity of their symptoms within minutes. A study in npj Digital Medicine describes the new tool, which has users tap their fingers 10 times in front of a webcam to assess motor performance on a scale of 0-4.
Doctors often have patients perform simple motor tasks to assess movement disorders and rate the severity using guidelines such as the Movement Disorder Society Unified Parkinson’s Disease Rating Scale (MDS-UPDRS). The AI model provides a rapid assessment using the MDS-UPDRS guidelines, automatically generating computational metrics such as speed, amplitude, frequency, and period that are interpretable, standardized, repeatable, and consistent with medical guidebooks. It uses those attributes to classify the severity of tremors.
The finger-tapping task was performed by 250 global participants with Parkinson’s disease and the AI system’s ratings were compared with those by three neurologists and three primary care physicians. While expert neurologists performed slightly better than the AI model, the AI model outperformed the primary care physicians with UPDRS certification.
The AI-based Parkinson’s disease severity test generates computational metrics such as speed, amplitude, frequency, and period, and uses those attributes to classify the severity of tremors. 
“These findings could have huge implications for patients who have difficulty gaining access to neurologists, getting appointments, and traveling to the hospital,” says Ehsan Hoque, an associate professor in Rochester’s Department of Computer Science and co-director of the Rochester Human-Computer Interaction Laboratory. “It’s an example of how AI is being gradually introduced into health care to serve people outside of the clinic and improve health equity and access.”
The study was led by Md. Saiful Islam, a Google PhD fellow and a graduate student in computer science advised by Hoque. The team of computer scientists collaborated with several members of the Medical Center’s Department of Neurology, including associate professor Jamie Adams; Ray Dorsey, the David M. Levy Professor of Neurology; and associate professor Ruth Schneider.
The researchers say their method can be applied to other motor tasks, which opens the door to evaluating other types of movement disorders such as ataxia and Huntington’s disease. The new Parkinson’s disease assessment is available online, though the researchers caution that it reflects an emerging technology and at this early stage should not be considered, on its own and without a physician’s input, as a definitive measure of the presence or severity of the disease. More

Researchers from the RIKEN Center for Quantum Computing have used machine learning to perform error correction for quantum computers — a crucial step for making these devices practical — using an autonomous correction system that despite being approximate, can efficiently determine how best to make the necessary corrections.
In contrast to classical computers, which operate on bits that can only take the basic values 0 and 1, quantum computers operate on “qubits,” which can assume any superposition of the computational basis states. In combination with quantum entanglement, another quantum characteristic that connects different qubits beyond classical means, this enables quantum computers to perform entirely new operations, giving rise to potential advantages in some computational tasks, such as large-scale searches, optimization problems, and cryptography.
The main challenge towards putting quantum computers into practice stems from the extremely fragile nature of quantum superpositions. Indeed, tiny perturbations induced, for instance, by the ubiquitous presence of an environment give rise to errors that rapidly destroy quantum superpositions and, as a consequence, quantum computers lose their edge.
To overcome this obstacle, sophisticated methods for quantum error correction have been developed. While they can, in theory, successfully neutralize the effect of errors, they often come with a massive overhead in device complexity, which itself is error-prone and thus potentially even increases the exposure to errors. As a consequence, full-fledged error correction has remained elusive.
In this work, the researchers leveraged machine learning in a search for error correction schemes that minimize the device overhead while maintaining good error correcting performance. To this end, they focused on an autonomous approach to quantum error correction, where a cleverly designed, artificial environment replaces the necessity to perform frequent error-detecting measurements. They also looked at “bosonic qubit encodings,” which are, for instance, available and utilized in some of the currently most promising and widespread quantum computing machines based on superconducting circuits.
Finding high-performing candidates in the vast search space of bosonic qubit encodings represents a complex optimization task, which the researchers address with reinforcement learning, an advanced machine learning method, where an agent explores a possibly abstract environment to learn and optimize its action policy. With this, the group found that a surprisingly simple, approximate qubit encoding could not only greatly reduce the device complexity compared to other proposed encodings, but also outperformed its competitors in terms of its capability to correct errors.
Yexiong Zeng, the first author of the paper, says, “Our work not only demonstrates the potential for deploying machine learning towards quantum error correction, but it may also bring us a step closer to the successful implementation of quantum error correction in experiments.”
According to Franco Nori, “Machine learning can play a pivotal role in addressing large-scale quantum computation and optimization challenges. Currently, we are actively involved in a number of projects that integrate machine learning, artificial neural networks, quantum error correction, and quantum fault tolerance.” More

Scientists have achieved a breakthrough in the development of non-volatile phase change memory−−a type of electronic memory that can store data even when the power is turned off−−in a material that has never displayed the sort of characteristics that such memory requires.
Until now, phase change memory has primarily been developed using chalcogenides−−a group of materials known to exhibit reversible electrical changes when they transition between their crystalline and amorphous states.
But what if there’s an even better material out there?
In a recently published study, researchers report a thermally reversible switching of room-temperature electrical resistivity in a layered nickelate−−potentially offering better performance and superior sustainability.
The study was published in the journal Advanced Science on September 3, 2023.
Layered nickelates are a class of complex oxide materials composed of nickel ions. They exhibit a layered structure, where planes of nickel and oxygen atoms are interspersed with layers containing other elements, often alkaline-earth or rare-earth elements. Their unique layered arrangement has drawn the interest of researchers due to the intriguing properties of their electrons, with potential applications in fields such as superconductivity and, in this case, electronics.
The researchers’ particular layered nickelate is composed of layers of of strontium, bismuth and oxygen atoms in a ‘rock salt’ structural arrangement, interleaved with layers of molecules of strontium, nickel and oxygen atoms in a perovskite structure. Perovskites are defined by a specific crystal structure of two positively charged atoms and one negatively charged one, and have a number of intriguing properties, from superconductivity to ferroelectricity−−a spontaneous electric polarization that can be reversed by the application of an electric field. More

A team of scientists from Ames National Laboratory developed a new machine learning model for discovering critical-element-free permanent magnet materials. The model predicts the Curie temperature of new material combinations. It is an important first step in using artificial intelligence to predict new permanent magnet materials. This model adds to the team’s recently developed capability for discovering thermodynamically stable rare earth materials.
High performance magnets are essential for technologies such as wind energy, data storage, electric vehicles, and magnetic refrigeration. These magnets contain critical materials such as cobalt and rare earth elements like Neodymium and Dysprosium. These materials are in high demand but have limited availability. This situation is motivating researchers to find ways to design new magnetic materials with reduced critical materials.
Machine learning (ML) is a form of artificial intelligence. It is driven by computer algorithms that use data and trial-and-error algorithms to continually improve its predictions. The team used experimental data on Curie temperatures and theoretical modeling to train the ML algorithm. Curie temperature is the maximum temperature at which a material maintains its magnetism.
“Finding compounds with the high Curie temperature is an important first step in the discovery of materials that can sustain magnetic properties at elevated temperatures,” said Yaroslav Mudryk, a scientist at Ames Lab and senior leader of the research team. “This aspect is critical for the design of not only permanent magnets but other functional magnetic materials.”
According to Mudryk, discovering new materials is a challenging activity because the search is traditionally based on experimentation, which is expensive and time-consuming. However, using a ML method can save time and resources.
Prashant Singh, a scientist at Ames Lab and member of the research team, explained that a major part of this effort was to develop an ML model using fundamental science. The team trained their ML model using experimentally known magnetic materials. The information about these materials establishes a relationship between several electronic and atomic structure features and Curie temperature. These patterns give the computer a basis for finding potential candidate materials.
To test the model, the team used compounds based on Cerium, Zirconium, and Iron. This idea was proposed by Andriy Palasyuk, a scientist at Ames Lab and member of the research team. He wanted to focus on unknown magnet materials based on earth-abundant elements. “The next super magnet must not only be superb in performance, but also rely on abundant domestic components,” said Palasyuk.
Palasyuk worked with Tyler Del Rose, another scientist at Ames Lab and member of the research team, to synthesize and characterize the alloys. They found that the ML model was successful in predicting the Curie temperature of material candidates. This success is an important first step in creating a high-throughput way of designing new permanent magnets for future technological applications.
“We are writing physics-informed machine learning for a sustainable future,” said Singh. More

Beyond-silicon technology demands ultra-high-performance field-effect transistors (FETs). Transition metal dichalcogenides (TMDs) provide an ideal material platform, but the device performances such as contact resistance, on/off ratio, and mobility are often limited by the presence of interfacial residues caused by transfer procedures. We show an ideal residue-free transfer approach using polypropylene carbonate (PPC) with a negligible residue for monolayer MoS2. By incorporating bismuth semimetal contact with atomically clean monolayer MoS2-FET on h-BN substrate, we obtain an ultralow Ohmic contact resistance approaching the quantum limit and a record-high on/off ratio of ~1011 at 15 K. Such an ultraclean fabrication approach could be the ideal platform for high-performance electrical devices using large-area semiconducting TMDs.
A revolution in technology is on the horizon, and it’s poised to change the devices that we use. Under the distinguished leadership of Professor LEE Young Hee, a team of visionary researchers from the Center for Integrated Nanostructure Physics within the Institute for Basic Science (IBS), South Korea, has unveiled a new discovery that can greatly improve the fabrication of field-effect transistors (FET).
A high-performance field-effect transistor (FET) is an essential building block for next-generation beyond-silicon-based semiconductor technologies. Current 3-dimensional silicon technology suffers from degradation of FET performances when the device is miniaturized past sub-3-nm scales. To overcome this limit, researchers have studied one-atom thick (~0.7 nm) two-dimensional (2D) transition metal dichalcogenides (TMDs) as an ideal FET platform over the last decade. Nevertheless, their practical applications are limited due to the inability to demonstrate integration at the wafer-scale.
A major problem is the residues that occur during fabrication. Traditionally, polymethyl methacrylate (PMMA) is used as a supporting holder for device transfer. This material is notorious for leaving insulating residues on TMD surfaces, which often generates mechanical damage to the fragile TMD sheet during transfer. As an alternative to PMMA, several other polymers such as polydimethylsiloxane (PDMS), polyvinyl alcohol (PVA), polystyrene (PS), polycarbonate (PC), ethylene vinyl acetate (EVA), polyvinylpyrrolidone (PVP) and organic molecules including paraffin, cellulose acetate, naphthalene have all been proposed as a supporting holder. Nevertheless, residues and mechanical damages are inevitably introduced during transfer, which leads to degradation of FET performances.
The IBS researchers addressed this problem and have made an intriguing breakthrough by successfully harnessing polypropylene carbonate (PPC) for residue-free wet transfer. Using PPC not only eliminated residue but also allowed for the production of wafer-scale TMD using chemical vapor deposition. Previous attempts at manufacturing large-scale TMDs often resulted in wrinkles, which occur during the transfer process. The weak binding affinity between the PPC and the TMD not only eliminated residues but wrinkles as well.
Mr. Ashok MONDAL, the first author of the study said, “The PPC transfer method we chose enables us to fabricate centimeter-scale TMDs. Previously, TMD was limited to being produced using a stamping method, which generates flakes that are only 30-40 μm in size.”
The researchers built a FET device using a semimetal Bi contact electrode with a monolayer of MoS2, which was transferred by the PPC method. Less than 0.08% of PPC residue was found to remain on the MoS2 layer. Thanks to the lack of interfacial residues, the device was found to have an ohmic contact resistance of RC ~78 Ω-µm, which is close to the quantum limit. An ultrahigh current on/off ratio of ~1011 at 15 K and a high on-current of ~1.4 mA/µm were also achieved using the h-BN substrate.
This finding was the first in the world that demonstrated wafer-scale production and transfer of CVD-grown TMD. The state-of-the-art FET device produced in this way was found to have electrical properties that far exceed that of previously reported values. It is believed that this technology can be easily implemented using the currently available integrated circuit manufacturing technology.
Dr. Chandan BISWAS, the co-corresponding author of the study said, “It is hoped that our success in the residue-free PPC transfer technique will encourage other researchers to develop further improvements in various TMD devices in the future.” More