Aurelia Butler

In materials science, candidates for novel functional materials are usually explored in a trial-and-error fashion through calculations, synthetic methods, and material analysis. However, the approach is time-consuming and requires expertise. Now, researchers from Japan have used a data-driven approach to automate the process of predicting new magnetic materials. By combining first-principles calculations, Bayesian optimization, and monoatomic alternating deposition, the proposed method can enable a faster development of next-generation electronic devices.
Materials scientists are constantly on the lookout for new “functional materials” with favorable properties directed towards some application. For instance, finding novel functional magnetic materials could open doors to energy-efficient spintronic devices. In recent years, the development of spintronics devices like magnetoresistive random access memory — an electronic device in which a single magnetoresistive element is integrated as one bit of information — has been progressing rapidly, for which magnetic materials with high magnetocrystalline anisotropy (MCA) are required. Ferromagnetic materials, which retain their magnetization without an external magnetic field, are of particular interest as data storage systems, therefore. For instance, L10-type ordered alloys consisting of two elements and two periods, such as L10-FeCo and L10-FeNi, have been studied actively as promising candidates for next-generation functional magnetic materials. However, the combination of constituent elements is extremely limited, and materials with extended element type, number, and periodicity have rarely been explored.
What impedes this exploration? Scientists point at combinatorial explosions that can occur easily in multilayered films, requiring a great deal of time and effort in the selection of the constituent elements and material fabrication, as the major reason. Besides, it is extremely difficult to predict the function of MCA because of the complex interplay of various parameters including crystal structure, magnetic moment, and electronic state, and the conventional protocol relies largely on trial and error. Thus, there is much scope and need for developing an efficient route to discovering new high-performance magnetic materials.
On this front, a team of researchers from Japan including Prof. Masato Kotsugi, Mr. Daigo Furuya, and Mr. Takuya Miyashita from Tokyo University of Science (TUS), along with Dr. Yoshio Miura from the National Institute for Materials Science (NIMS), has now turned to a data-driven approach for automating the prediction and synthesis of new magnetic materials. In a new study, which was made available online on June 30, 2022 and published in Science and Technology of Advanced Materials: Methods on July 1, 2022, the team reported their success in the development of material exploration system by integrating computational, information, and experimental sciences for high MCA magnetic materials. Prof. Kotsugi explains, “We have focused on artificial intelligence and have combined it with computational and experimental science to develop an efficient material synthesis method. Promising materials beyond human expectation have been discovered in terms of electronic structure. Thus, it will change the nature of materials engineering!”
In their study, which was the result of joint research by TUS and NIMS and supported by JST-CREST, the team calculated MCA energy through first-principles calculations (a method used to calculate electronic states and physical properties in materials based on the laws of quantum mechanics) and performed Bayesian optimization to search for materials with high MCA energy. After examining the algorithm for Bayesian optimization, they found promising materials five times more efficiently than through the conventional trial-and-error approach. This robust material search method was less susceptible to influences from irregular factors like outliers and noise and allowed the team to select the top three candidate materials — (Fe/Cu/Fe/Cu), (Fe/Cu/Co/Cu), and (Fe/Co/Fe/Ni) — comprising iron (Fe), cobalt (Co), nickel (Ni), and copper (Cu).
The top three predicted materials with the largest MCA energy values were then fabricated via the monoatomic alternating stacking method using the laser-driven pulsed deposition technique to create multilayered magnetic materials consisting of 52 layers, namely [Fe/Cu/Fe/Cu]13, [Fe/Cu/Co/Cu]13, and [Fe/Co/Fe/Ni]13. Among the three structures, [Fe/Co/Fe/Ni]1 showed an MCA value (3.74 × 106 erg/cc) much above that of L10-FeNi (1.30 × 106 erg/cc).
Furthermore, using the second-order perturbation method, the team found that MCA is generated in the electronic state, which has not been realized in previously reported materials. This attests to the suitability of employing Bayesian optimization to identify electronic states that are likely impossible to envision through human experience and intuition alone. Thus, the developed method can autonomously search for suitable elements to design functional magnetic materials. “This technique is extendable to advanced magnetic materials with more complicated electronic correlations, such as Heusler alloys and spin-thermoelectric materials,” observes Prof. Kotsugi. More

When robots appear to engage with people and display human-like emotions, people may perceive them as capable of “thinking,” or acting on their own beliefs and desires rather than their programs, according to research published by the American Psychological Association.
“The relationship between anthropomorphic shape, human-like behavior and the tendency to attribute independent thought and intentional behavior to robots is yet to be understood,” said study author Agnieszka Wykowska, PhD, a principal investigator at the Italian Institute of Technology. “As artificial intelligence increasingly becomes a part of our lives, it is important to understand how interacting with a robot that displays human-like behaviors might induce higher likelihood of attribution of intentional agency to the robot.”
The research was published in the journal Technology, Mind, and Behavior.
Across three experiments involving 119 participants, researchers examined how individuals would perceive a human-like robot, the iCub, after socializing with it and watching videos together. Before and after interacting with the robot, participants completed a questionnaire that showed them pictures of the robot in different situations and asked them to choose whether the robot’s motivation in each situation was mechanical or intentional. For example, participants viewed three photos depicting the robot selecting a tool and then chose whether the robot “grasped the closest object” or “was fascinated by tool use.”
In the first two experiments, the researchers remotely controlled iCub’s actions so it would behave gregariously, greeting participants, introducing itself and asking for the participants’ names. Cameras in the robot’s eyes were also able to recognize participants’ faces and maintain eye contact. The participants then watched three short documentary videos with the robot, which was programmed to respond to the videos with sounds and facial expressions of sadness, awe or happiness.
In the third experiment, the researchers programmed iCub to behave more like a machine while it watched videos with the participants. The cameras in the robot’s eyes were deactivated so it could not maintain eye contact and it only spoke recorded sentences to the participants about the calibration process it was undergoing. All emotional reactions to the videos were replaced with a “beep” and repetitive movements of its torso, head and neck.
The researchers found that participants who watched videos with the human-like robot were more likely to rate the robot’s actions as intentional, rather than programmed, while those who only interacted with the machine-like robot were not. This shows that mere exposure to a human-like robot is not enough to make people believe it is capable of thoughts and emotions. It is human-like behavior that might be crucial for being perceived as an intentional agent.
According to Wykowska, these findings show that people might be more likely to believe artificial intelligence is capable of independent thought when it creates the impression that it can behave just like humans. This could inform the design of social robots of the future, she said.
“Social bonding with robots might be beneficial in some contexts, like with socially assistive robots. For example, in elderly care, social bonding with robots might induce a higher degree of compliance with respect to following recommendations regarding taking medication,” Wykowska said. “Determining contexts in which social bonding and attribution of intentionality is beneficial for the well-being of humans is the next step of research in this area.”
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In 2004, researchers from the University of Manchester used adhesive tape to pull sheets of single carbon atoms away from graphite to make graphene — a material that is 1000 times thinner than human hair yet stronger than steel. This ground-breaking exfoliation technique paved the way for the development of a wide range of two-dimensional materials with distinct electrical and physical characteristics for the next generation of electronic devices.
One such material of interest has been muscovite mica (MuM). These minerals have the general formula KAl2(AlSi3O10) (F, OH)2 and have a layered structure consisting of aluminum (Al), potassium (K), and silicon (Si). Like graphene, MuM has gained attention as an ultra-flat substrate for building flexible electronic devices. Unlike graphene, however, MuM is an insulator.
However, the electrical properties of MuM are not altogether clear. In particular, the properties of single and few-molecule-layer thick MuMs are not clearly understood. This is because in all the studies that have probed the electrical properties of MuM so far, the conductivity has been dominated by a quantum phenomenon called “tunneling.” This has made it difficult to understand the conductive nature of thin MuM.
In a recent study published in the journal Physical Review Applied, Professor Muralidhar Miryala from Shibaura Institute of Technology (SIT), Japan, along with Professors M. S. Ramachandra Rao, Ananth Krishnan and Mr. Ankit Arora, a PhD student, from Indian Institute of Technology Madras, India, have now observed a semiconducting behavior in thin MuM flakes, characterized by an electrical conductivity that is 1000 times larger than that of thick MuM. “Mica has been one of the most popular electrical insulators used in industries for decades. However, this semiconductor-like behavior has not been reported earlier,” says Prof. Miryala.
In their study, the researchers exfoliated thin MuM flakes of varying thickness onto silicon (SiO2/Si) substrates and, to avoid tunneling, maintained a separation of 1 µm between the contact electrodes. On measuring the electrical conductivity, they noticed that the transition to a conducting state occurred gradually as the flakes were thinned down to fewer layers. They found that for MuM flakes below 20 nm, the current depended on the thickness, becoming 1000 times larger for a 10 nm thick MuM (5 layers thick) compared to that in 20 nm MuM.
To make sense of this result, the researchers fitted the experimental conductivity data to a theoretical model called the “hopping conduction model,” which suggested that the observed conductance is due to an increase in the conduction band carrier density with the reduction in thickness. Put simply, as the thickness of MuM flakes is reduced, the energy required to move electrons from the solid bulk to the surface decreases, allowing the electrons easier passage into the “conduction band,” where they can freely move to conduct electricity. As to why the carrier density increases, the researchers attributed it to the effects of surface doping (impurity addition) contributions from K+ ions and relaxation of the MuM crystal structure.
The significance of this finding is that thin exfoliated sheets of MuM have a band structure similar to that of wide bandgap semiconductors. This, combined with its exceptional chemical stability, makes thin MuM flakes an ideal material for two-dimensional electronic devices that are both flexible and durable. “MuM is known for its exceptional stability in harsh environments such as those characterized by high temperatures, pressures, and electrical stress. The semiconductor-like behavior observed in our study indicates that MuM has the potential to pave the way for the development of robust electronics,” says Prof. Miryala.
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A discovery about how algorithms can learn and retain information more efficiently offers potential insight into the brain’s ability to absorb new knowledge. The findings by researchers at the University of California, Irvine School of Biological Sciences could aid in combatting cognitive impairments and improving technology. Their study appears in Proceedings of the National Academy of Sciences.
The scientists focused on artificial neural networks, known as ANNs, which are algorithms designed to emulate the behavior of brain neurons. Like human minds, ANNs can absorb and classify vast quantities of information. Unlike our brains, however, ANNs tend to forget what they already know when fresh knowledge is introduced too fast, a phenomenon known as catastrophic forgetting.
Researchers have long theorized that our ability to learn new concepts stems from the interplay between the brain’s hippocampus and the neocortex. The hippocampus captures fresh information and replays it during rest and sleep. The neocortex grabs the new material and reviews its existing knowledge so it can interleave, or layer, the fresh material into similar categories developed from the past.
However, there has been some question about this process, given the excessive amount of time it would take the brain to sort through the whole trove of information it has gathered during a lifetime. This pitfall could explain why ANNs lose long-term knowledge when absorbing new data too quickly.
Traditionally, the solution used in deep machine learning has been to retrain the network on the entire set of past data, whether or not it was closely related to the new information, a very time-consuming process. The UCI scientists decided to examine the issue in greater depth and made a notable discovery.
“We found that when ANNs interleaved a much smaller subset of old information, including mainly items that were similar to the new knowledge they were acquiring, they learned it without forgetting what they already knew,” said graduate student Rajat Saxena, the paper’s first author. Saxena spearheaded the project with assistance from Justin Shobe, an assistant project scientist. Both members of the laboratory of Bruce McNaughton, Distinguished Professor of neurobiology & behavior.
“It allowed ANNs to take in fresh information very efficiently, without having to review everything they had previously acquired,” Saxena said. “These findings suggest a brain mechanism for why experts at something can learn new things in that area much faster than non-experts. If the brain already has a cognitive framework related to the new information, the new material can be absorbed more quickly because changes are only needed in the part of brain’s network that encodes the expert knowledge.”
The discovery holds potential for tackling cognitive issues, according to McNaughton. “Understanding the mechanisms behind learning is essential for making progress,” he said. “It gives us insights into what’s going on when brains don’t work the way they are supposed to. We could develop training strategies for people with memory problems from aging or those with brain damage. It could also lead to the ability to manipulate brain circuits so people can overcome these deficits.”
The findings offer possibilities as well for making algorithms in machines such as medical diagnostic equipment, autonomous cars and many others more precise and efficient.
Funding for the research was provided by a Defense Advanced Research Projects Agency Grant in support of basic research of potential benefit to humankind and by the National Institutes of Health.
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If you switch a bit in the memory of a computer and then switch it back again, you have restored the original state. There are only two states that can be called “0 and 1.”
However, an amazing effect has now been discovered at TU Wien (Vienna): In a crystal based on oxides of gadolinium and manganese, an atomic switch was found that has to be switched back and forth not just once, but twice, until the original state is reached again. During this double switching-on and switching-off process, the spin of gadolinium atoms performs one full rotation. This is reminiscent of a crankshaft, in which an up-and-down movement is converted into a circular movement.
This new phenomenon opens up interesting possibilities in material physics, even information could be stored with such systems. The strange atomic switch has now been presented in the scientific journal Nature.
Coupling of electrical and magnetic properties
Normally, a distinction is made between the electrical and magnetic properties of materials. Electrical properties are based on the fact that charge carriers move — for example electrons that travel through a metal or ions whose position is shifted.
Magnetic properties, on the other hand, are closely related to the spin of atoms — the particle’s intrinsic angular momentum, which can point in a very specific direction, much like the Earth’s axis of rotation points in a very specific direction. More

Though they are discrete particles, water molecules flow collectively as liquids, producing streams, waves, whirlpools, and other classic fluid phenomena.
Not so with electricity. While an electric current is also a construct of distinct particles — in this case, electrons — the particles are so small that any collective behavior among them is drowned out by larger influences as electrons pass through ordinary metals. But, in certain materials and under specific conditions, such effects fade away, and electrons can directly influence each other. In these instances, electrons can flow collectively like a fluid.
Now, physicists at MIT and the Weizmann Institute of Science have observed electrons flowing in vortices, or whirlpools — a hallmark of fluid flow that theorists predicted electrons should exhibit, but that has never been seen until now.
“Electron vortices are expected in theory, but there’s been no direct proof, and seeing is believing,” says Leonid Levitov, professor of physics at MIT. “Now we’ve seen it, and it’s a clear signature of being in this new regime, where electrons behave as a fluid, not as individual particles.”
The observations, reported in the journal Nature, could inform the design of more efficient electronics.
“We know when electrons go in a fluid state, [energy] dissipation drops, and that’s of interest in trying to design low-power electronics,” Levitov says. “This new observation is another step in that direction.”
Levitov is a co-author of the new paper, along with Eli Zeldov and others at the Weizmann Institute for Science in Israel and the University of Colorado at Denver. More

Big data has become a big challenge for space scientists analyzing vast datasets from increasingly powerful space instrumentation. To address this, a Southwest Research Institute team has developed a machine learning tool to efficiently label large, complex datasets to allow deep learning models to sift through and identify potentially hazardous solar events. The new labeling tool can be applied or adapted to address other challenges involving vast datasets.
As space instrument packages collect increasingly complex data in ever-increasing volumes, it is becoming more challenging for scientists to process and analyze relevant trends. Machine learning (ML) is becoming a critical tool for processing large complex datasets, where algorithms learn from existing data to make decisions or predictions that can factor more information simultaneously than humans can. However, to take advantage of ML techniques, humans need to label all the data first — often a monumental endeavor.
“Labeling data with meaningful annotations is a crucial step of supervised ML. However, labeling datasets is tedious and time consuming,” said Dr. Subhamoy Chatterjee, a postdoctoral researcher at SwRI specializing in solar astronomy and instrumentation and lead author of a paper about these findings published in the journal Nature Astronomy. “New research shows how convolutional neural networks (CNNs), trained on crudely labeled astronomical videos, can be leveraged to improve the quality and breadth of data labeling and reduce the need for human intervention.”
Deep learning techniques can automate processing and interpret large amounts of complex data by extracting and learning complex patterns. The SwRI team used videos of the solar magnetic field to identify areas where strong, complex magnetic fields emerge on the solar surface, which are the main precursor of space weather events.
“We trained CNNs using crude labels, manually verifying only our disagreements with the machine,” said co-author Dr. Andrés Muñoz-Jaramillo, an SwRI solar physicist with expertise in machine learning. “We then retrained the algorithm with the corrected data and repeated this process until we were all in agreement. While flux emergence labeling is typically done manually, this iterative interaction between the human and ML algorithm reduces manual verification by 50%.”
Iterative labeling approaches such as active learning can significantly save time, reducing the cost of making big data ML ready. Furthermore, by gradually masking the videos and looking for the moment where the ML algorithm changes its classification, SwRI scientists further leveraged the trained ML algorithm to provide an even richer and more useful database.
“We created an end-to-end, deep-learning approach for classifying videos of magnetic patch evolution without explicitly supplying segmented images, tracking algorithms or other handcrafted features,” said SwRI’s Dr. Derek Lamb, a co-author specializing in evolution of magnetic fields on the surface of the Sun. “This database will be critical in the development of new methodologies for forecasting the emergence of the complex regions conducive to space weather events, potentially increasing the lead time we have to prepare for space weather.”
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Physicists at Indiana University and the University of Tennessee have cracked the code to making microchips smaller, and the key is helium.
Microchips are everywhere, running computers and cars, and even helping people find lost pets. As microchips grow smaller, faster and capable of doing more things, the wires that conduct electricity to them must follow suit. But there’s a physical limit to how small they can become — unless they are designed differently.
“In a traditional system, as you put more transistors on, the wires get smaller,” said Paul Sokol, a professor in the IU Bloomington College of Arts and Sciences’ Department of Physics. “But under newly designed systems, it’s like confining the electrons in a one-dimensional tube, and that behavior is quite different from a regular wire.”
To study the behavior of particles under these circumstances, Sokol collaborated with a physics professor at the University of Tennessee, Adrian Del Maestro, to create a model system of electronics packed into a one-dimensional tube.
Their findings were recently published in Nature Communications.
The pair used helium to create a model system for their study because its interactions with electrons are well known, and it can be made extremely pure, Sokol said. However, there were issues with using helium in a one-dimensional space, the first being that no one had ever done it before. More