Aurelia Butler

Holding the right material at the right angle, Cornell researchers have discovered a strategy to switch the magnetization in thin layers of a ferromagnet — a technique that could eventually lead to the development of more energy-efficient magnetic memory devices.
The team’s paper, “Tilted Spin Current Generated by the Collinear Antiferromagnet Ruthenium Dioxide,” published May 5 in Nature Electronics. The paper’s co-lead authors are postdoctoral researcher Arnab Bose and doctoral students Nathaniel Schreiber and Rakshit Jain.
For decades, physicists have tried to change the orientation of electron spins in magnetic materials by manipulating them with magnetic fields. But researchers including Dan Ralph, the F.R. Newman Professor of Physics in the College of Arts and Sciences and the paper’s senior author, have instead looked to using spin currents carried by electrons, which exist when electrons have spins generally oriented in one direction.
When these spin currents interact with a thin magnetic layer, they transfer their angular momentum and generate enough torque to switch the magnetization 180 degrees. (The process of switching this magnetic orientation is how one writes information in magnetic memory devices.)
Ralph’s group has focused on finding ways to control the direction of the spin in spin currents by generating them with antiferromagnetic materials. In antiferromagnets, every other electron spin points in the opposite direction, hence there is no net magnetization.
“Essentially, the antiferromagnetic order can lower the symmetries of the samples enough to allow unconventional orientations of spin current to exist,” Ralph said. “The mechanism of antiferromagnets seems to give a way of actually getting fairly strong spin currents, too.”
The team had been experimenting with the antiferromagnet ruthenium dioxide and measuring the ways its spin currents tilted the magnetization in a thin layer of a nickel-iron magnetic alloy called Permalloy, which is a soft ferromagnet. In order to map out the different components of the torque, they measured its effects at a variety of magnetic field angles. More

Researchers at the University of Missouri are applying a form of artificial intelligence (AI) — previously used to analyze how National Basketball Association (NBA) players move their bodies — to now help scientists develop new drug therapies for medical treatments targeting cancers and other diseases.
The type of AI, called a graph neural network, can help scientists with speeding up the time it takes to sift through large amounts of data generated by studying protein dynamics. This approach can provide new ways to identify target sites on proteins for drugs to work effectively, said Dong Xu, a Curators’ Distinguished Professor in the Department of Electrical Engineering and Computer Science at the MU College of Engineering and one of the study’s authors.
“Previously, drug designers may have known about a couple places on a protein’s structure to target with their therapies,” said Xu, who is also the Paul K. and Dianne Shumaker Professor in bioinformatics. “A novel outcome of this method is that we identified a pathway between different areas of the protein structure, which could potentially allow scientists who are designing drugs to see additional possible target sites for delivering their targeted therapies. This can increase the chances that the therapy may be successful.”
Xu said they can also simulate how proteins can change in relation to different conditions, such as the development of cancer, and then use that information to infer their relationships with other bodily functions.
“With machine learning we can really study what are the important interactions within different areas of the protein structure,” Xu said. “Our method provides a systematic review of the data involved when studying proteins, as well as a protein’s energy state, which could help when identifying any possible mutation’s effect. This is important because protein mutations can enhance the possibility of cancers and other diseases developing in the body.”
“Neural relational inference to learn long-range allosteric interactions in proteins from molecular dynamics simulations” was published in Nature Communications. Juexin Wang at MU; and Jingxuan Zhu and Weiwei Han at Jilin University in China, also contributed to this study. Funding was provided by the China Scholarship Council and the Overseas Cooperation Project of Jilin Province, which were used to support Jingxuan Zhu to conduct this research at MU, as well as the National Institute of General Medical Sciences of the National Institutes of Health. The content is solely the responsibility of the authors and does not necessarily represent the official views of the funding agencies.
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Materials provided by University of Missouri-Columbia. Note: Content may be edited for style and length. More

Rice University photonics researchers have created a potentially disruptive technology for the ultraviolet optics market.
By precisely etching hundreds of tiny triangles on the surface of a microscopic film of zinc oxide, nanophotonics pioneer Naomi Halas and colleagues created a “metalens” that transforms incoming long-wave UV (UV-A) into a focused output of vacuum UV (VUV) radiation. VUV is used in semiconductor manufacturing, photochemistry and materials science and has historically been costly to work with, in part because it is absorbed by almost all types of glass used to make conventional lenses.
“This work is particularly promising in light of recent demonstrations that chip manufacturers can scale up the production of metasurfaces with CMOS-compatible processes,” said Halas, co-corresponding author of a metalens demonstration study published in Science Advances. “This is a fundamental study, but it clearly points to a new strategy for high-throughput manufacturing of compact VUV optical components and devices.”
Halas’ team showed its microscopic metalens could convert 394-nanometer UV into a focused output of 197-nanometer VUV. The disc-shaped metalens is a transparent sheet of zinc oxide that is thinner than a sheet of paper and just 45 millionths of a meter in diameter. In the demonstration, a 394-nanometer UV-A laser was shined at the back of the disc, and researchers measured the light that emerged from the other side.
Study co-first author Catherine Arndt, an applied physics graduate student in Halas’ research group, said the key feature of the metalens is its interface, a front surface that is studded with concentric circles of tiny triangles.
“The interface is where all of the physics is happening,” she said. “We’re actually imparting a phase shift, changing both how quickly the light is moving and the direction it’s traveling. We don’t have to collect the light output because we use electrodynamics to redirect it at the interface where we generate it.”
Violet light has the lowest wavelength visible to humans. Ultraviolet has even lower wavelengths, which range from 400 nanometers to 10 nanometers. Vacuum UV, with wavelengths between 100-200 nanometers, is so-named because it is strongly absorbed by oxygen. Using VUV light today typically requires a vacuum chamber or other specialized environment, as well as machinery to generate and focus VUV. More

Funded by the National Science Foundation’s Designing Materials to Revolutionize Our Engineering Future (DMREF) Program, researchers from the Department of Materials Science and Engineering at Texas A&M University used an Artificial Intelligence Materials Selection framework (AIMS) to discover a new shape memory alloy. The shape memory alloy showed the highest efficiency during operation achieved thus far for nickel-titanium-based materials. In addition, their data-driven framework offers proof of concept for future materials development.
Shape memory alloys are utilized in various fields where compact, lightweight and solid-state actuations are needed, replacing hydraulic or pneumatic actuators because they can deform when cold and then return to their original shape when heated. This unique property is critical for applications, such as airplane wings, jet engines and automotive components, that must withstand repeated, recoverable large-shape changes.
There have been many advancements in shape memory alloys since their beginnings in the mid-1960s, but at a cost. Understanding and discovering new shape memory alloys has required extensive research through experimentation and ad-hoc trial and error. Despite many of which have been documented to help further shape memory alloy applications, new alloy discoveries have occurred in a decadal fashion. About every 10 years, a significant shape memory alloy composition or system has been discovered. Moreover, even with advances in shape memory alloys, they are hindered by their low energy efficiency caused by incompatibilities in their microstructure during the large shape change. Further, they are notoriously difficult to design from scratch.
To address these shortcomings, Texas A&M researchers have combined experimental data to create an AIMS computational framework capable of determining optimal materials compositions and processing these materials, which led to the discovery of a new shape memory alloy composition.
“When designing materials, sometimes you have multiple objectives or constraints that conflict, which is very difficult to work around,” said Dr. Ibrahim Karaman, Chevron Professor I and materials science and engineering department head. “Using our machine-learning framework, we can use experimental data to find hidden correlations between different materials’ features to see if we can design new materials.”
The shape memory alloy found during the study using AIMS was predicted and proven to achieve the narrowest hysteresis ever recorded. In other words, the material showed the lowest energy loss when converting thermal energy to mechanical work. The material showcased high efficiency when subject to thermal cycling due to its extremely small transformation temperature window. The material also exhibited excellent cyclic stability under repeated actuation.
A nickel-titanium-copper composition is typical for shape memory alloys. Nickel-titanium-copper alloys typically have titanium equal to 50% and form a single-phase material. Using machine learning, the researchers predicted a different composition with titanium equal to 47% and copper equal to 21%. While this composition is in the two-phase region and forms particles, they help enhance the material’s properties, explained William Trehern, doctoral student and graduate research assistant in the materials science and engineering department and the publication’s first author.
In particular, this high-efficiency shape memory alloy lends itself to thermal energy harvesting, which requires materials that can capture waste energy produced by machines and put it to use, and thermal energy storage, which is used for cooling electronic devices.
More notably, the AIMS framework offers the opportunity to use machine-learning techniques in materials science. The researchers see potential to discover more shape memory alloy chemistries with desired characteristics for various other applications.
“It is a revelation to use machine learning to find connections that our brain or known physical principles may not be able to explain,” said Karaman. “We can use data science and machine learning to accelerate the rate of materials discovery. I also believe that we can potentially discover new physics or mechanisms behind materials behavior that we did not know before if we pay attention to the connections machine learning can find.”
Other contributors include Dr. Raymundo Arróyave and Dr. Kadri Can Atli, professors in the materials science and engineering department, and materials science and engineering undergraduate student Risheil Ortiz-Ayala.
“While machine learning is now widely used in materials science, most approaches to date focus on predicting the properties of a material without necessarily explaining how to process it to achieve target properties,” said Arróyave. “Here, the framework looked not only at the chemical composition of candidate materials, but also the processing necessary to attain the properties of interest.”
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Materials provided by Texas A&M University. Original written by Michelle Revels. Note: Content may be edited for style and length. More

Bicycle sharing systems (BSSs) are transport solutions wherein users can rent a bicycle from a depot or ‘port,’ travel, and then return the bike to the same port or different port. BSSs are growing in popularity around the world because they are eco-friendly, reduce traffic congestion, and offer added health benefits to users. But eventually, a port becomes either full or empty in a BSS. This means that users are no longer able to rent a bike (when empty) or return one (when full). To address this issue, bikes need to be rebalanced among the ports in a BSS so that users are always able to use them. This rebalancing must also be carried out in a way that is beneficial to BSS companies so that they can reduce labor costs, as well as carbon emissions from rebalancing vehicles.
There are several existing approaches to BSS rebalancing, however, most solution algorithms are computationally expensive and take a lot of time to find an ‘exact’ solution in cases where there are a large number of ports. Even finding an approximate solution is computationally expensive. Previously, a research team led by Prof. Tohru Ikeguchi from Tokyo University of Science proposed a ‘multiple-vehicle bike sharing system routing problem with soft constraints’ (mBSSRP-S) that can find the shortest travel times for multiple bike rebalancing vehicles with the caveat that the optimal solution can sometimes violate the real-world limitations of the problem. Now, in a recent study published in MDPI’s Applied Sciences, the team has proposed two strategies to search for approximate solutions to the mBSSRP-S that can reduce computational costs without affecting performance. The research team also featured PhD student Ms. Honami Tsushima of Tokyo University of Science and Prof. Takafumi Matsuura of Nippon Institute of Technology.
Describing their research, Prof. Ikeguchi says, “Earlier, we had proposed the mBSSRP-S and that offered improved performance as compared to our original mBSSRP, which did not allow the violation of constraints. But the mBSSRP-S also increased the overall computational cost of the problem because it had to calculate both the feasible and infeasible solutions of the mBSSRP. Therefore, we have now proposed two consecutive search strategies to address this problem.”
The proposed search strategies look for feasible solutions in a much shorter period of time as compared to the one originally proposed with mBSSRP-S. The first strategy focuses on reducing the number of ‘neighboring’ solutions (solutions that are numerically close to a solution to the optimization problem) before finding a feasible solution. The strategy employs two well-known algorithms called ‘Or-opt’ and ‘CROSS-exchange,’ to reduce the overall time taken to compute a solution. The feasible solution here refers to values that satisfy the constraints of mBSSRP.
The second strategy changes the problem to be solved based on the feasible solution to either the mBSSRP problem or the mBSSRP-S problem and then searches for good near-optimal solutions in a short time by either Or-opt or CROSS-exchange.
The research team then performed numerical experiments to evaluate the computational cost and performance of their algorithms. “With the application of these two strategies, we have succeeded in reducing computational time while maintaining performance,” reveals Prof. Ikeguchi. “We also found that once we calculated the feasible solution, we could find short travel times for the rebalancing vehicles quickly by solving the hard constraint problem, mBSSRP, instead of mBSSRP-S.”
The popularity of BSSs is only expected to grow in the future. The new solution-search strategies proposed here will go a long way towards realizing convenient and comfortable BSSs that benefit users, companies, and the environment.
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Materials provided by Tokyo University of Science. Note: Content may be edited for style and length. More

Technique could reduce costs of battery development.
Imagine a psychic telling your parents, on the day you were born, how long you would live. A similar experience is possible for battery chemists who are using new computational models to calculate battery lifetimes based on as little as a single cycle of experimental data.
In a new study, researchers at the U.S. Department of Energy’s (DOE) Argonne National Laboratory have turned to the power of machine learning to predict the lifetimes of a wide range of different battery chemistries. By using experimental data gathered at Argonne from a set of 300 batteries representing six different battery chemistries, the scientists can accurately determine just how long different batteries will continue to cycle.
In a machine learning algorithm, scientists train a computer program to make inferences on an initial set of data, and then take what it has learned from that training to make decisions on another set of data.
“For every different kind of battery application, from cell phones to electric vehicles to grid storage, battery lifetime is of fundamental importance for every consumer,” said Argonne computational scientist Noah Paulson, an author of the study. “Having to cycle a battery thousands of times until it fails can take years; our method creates a kind of computational test kitchen where we can quickly establish how different batteries are going to perform.”
“Right now, the only way to evaluate how the capacity in a battery fades is to actually cycle the battery,” added Argonne electrochemist Susan “Sue” Babinec, another author of the study. “It’s very expensive and it takes a long time.”
According to Paulson, the process of establishing a battery lifetime can be tricky. “The reality is that batteries don’t last forever, and how long they last depends on the way that we use them, as well as their design and their chemistry,” he said. “Until now, there’s really not been a great way to know how long a battery is going to last. People are going to want to know how long they have until they have to spend money on a new battery.” More

Researchers have shown it is possible to perform artificial intelligence using tiny nanomagnets that interact like neurons in the brain.
The new method, developed by a team led by Imperial College London researchers, could slash the energy cost of artificial intelligence (AI), which is currently doubling globally every 3.5 months.
In a paper published today in Nature Nanotechnology, the international team have produced the first proof that networks of nanomagnets can be used to perform AI-like processing. The researchers showed nanomagnets can be used for ‘time-series prediction’ tasks, such as predicting and regulating insulin levels in diabetic patients.
Artificial intelligence that uses ‘neural networks’ aims to replicate the way parts of the brain work, where neurons talk to each other to process and retain information. A lot of the maths used to power neural networks was originally invented by physicists to describe the way magnets interact, but at the time it was too difficult to use magnets directly as researchers didn’t know how to put data in and get information out.
Instead, software run on traditional silicon-based computers was used to simulate the magnet interactions, in turn simulating the brain. Now, the team have been able to use the magnets themselves to process and store data — cutting out the middleman of the software simulation and potentially offering enormous energy savings.
Nanomagnetic states
Nanomagnets can come in various ‘states’, depending on their direction. Applying a magnetic field to a network of nanomagnets changes the state of the magnets based on the properties of the input field, but also on the states of surrounding magnets. More