MIT researchers discovered hidden magnetic properties in multilayer electronic material by analyzing polarized neutrons with neural networks. Credit: Ella Maru Studio

A[{” attribute=””>MIT team incorporates AI to facilitate the detection of an intriguing materials phenomenon that can lead to electronics without energy dissipation.

Superconductors have long been considered the principal approach for realizing electronics without resistivity. In the past decade, a new family of quantum materials, “topological materials,” has offered an alternative but promising means for achieving electronics without energy dissipation (or loss). Compared to superconductors, topological materials provide a few advantages, such as robustness against disturbances. To attain the dissipationless electronic states, one key route is the so-called “magnetic proximity effect,” which occurs when magnetism penetrates slightly into the surface of a topological material. However, observing the proximity effect has been challenging.

The problem, according to Zhantao Chen, a mechanical engineering PhD student at MIT, “is that the signal people are looking for that would indicate the presence of this effect is usually too weak to detect conclusively with traditional methods.” That’s why a team of scientists — based at MIT, Pennsylvania State University, and the National Institute of Standards and Technology — decided to try a nontraditional approach, which ended up yielding surprisingly good results.

What lies beneath, and between, the layers

For the past few years, researchers have relied on a technique known as polarized neutron reflectometry (PNR) to probe the depth-dependent magnetic structure of multilayered materials, as well as to look for phenomena such as the magnetic proximity effect. In PNR, two polarized neutron beams with opposing spins are reflected from the sample and collected on a detector. “If the neutron encounters a magnetic flux, such as that found inside a magnetic material, which has the opposite orientation, it will change its spin state, resulting in different signals measured from the spin up and spin down neutron beams,” explains Nina Andrejevic, PhD in materials science and engineering. As a result, the proximity effect can be detected if a thin layer of a normally nonmagnetic material — placed immediately adjacent to a magnetic material — is shown to become magnetized.

But the effect is very subtle, extending only about 1 nanometer in depth, and ambiguities and challenges can arise when it comes to interpreting experimental results. “By bringing machine learning into our methodology, we hoped to get a clearer picture of what’s going on,” notes Mingda Li, the Norman C. Rasmussen Career Development Professor in the Department of Nuclear Science and Engineering who headed the research team. That hope was indeed borne out, and the team’s findings were published on March 17, 2022, in a paper in Applied Physics Review.

The researchers investigated a topological insulator — a material that is electrically insulating in its interior but can conduct electric current on the surface. They chose to focus on a layered materials system comprising the topological insulator bismuth selenide (Bi₂Se₃) interfaced with the ferromagnetic insulator europium sulfide (EuS). Bi₂Se₃ is, by itself, a nonmagnetic material, so the magnetic EuS layer dominates the difference between the signals measured by the two polarized neutron beams. However, with the help of machine learning, the researchers were able to identify and quantify another contribution to the PNR signal — the magnetization induced in the Bi₂Se₃ at the interface with the adjoining EuS layer. “Machine learning methods are highly effective in eliciting underlying patterns from complex data, making it possible to discern subtle effects like that of proximity magnetism in the PNR measurement,” Andrejevic says.

When the PNR signal is first fed to the machine learning model, it is highly complex. The model is able to simplify this signal so that the proximity effect is amplified and thus becomes more conspicuous. Using this pared-down representation of the PNR signal, the model can then quantify the induced magnetization — indicating whether or not the magnetic proximity effect is observed — along with other attributes of the materials system, such as the thickness, density, and roughness of the constituent layers.

Better seeing through AI

“We’ve reduced the ambiguity that arose in previous analyses, thanks to the doubling in the resolution achieved using the machine learning-assisted approach,” say Leon Fan and Henry Heiberger, undergraduate researchers participating in this study. What that means is that they could discern materials properties at length scales of 0.5 nm, half of the typical spatial extent of proximity effect. That’s analogous to looking at writing on a blackboard from 20 feet away and not being able to make out any of the words. But if you could cut that distance in half, you might be able to read the whole thing.

The data analysis process can also be sped up significantly through a reliance on machine learning. “In the old days, you could spend weeks fiddling with all the parameters until you can get the simulated curve to fit the experimental curve,” Li says. “It can take many tries because the same [PNR] Signal could correspond to different combinations of parameters.”

“The neural network gives you an instant answer,” Chen adds. “There is no more guesswork. No more trial and error.” For this reason, the framework has been installed in some reflectometry beamlines to support the analysis of broader material types.

Some outside observers have praised the new study — it’s the first to assess the effectiveness of machine learning in identifying the proximity effect and among the first machine-learning-based packages to be used for PNR data analysis. “The work of Andrejevic et al. provides an alternative way to capture fine detail in PNR data and shows how higher resolution can be consistently achieved,” said Kang L. Wang, Distinguished Professor and Raytheon Professor of Electrical Engineering at the University of California, Los Angeles.

“This is really exciting progress,” commented Chris Leighton, Distinguished McKnight University Professor at the University of Minnesota. “Their new approach to machine learning could not only significantly speed up this process, but also extract even more material information from the available data.”

The MIT-led group is already considering expanding the scope of their investigation. “The magnetic proximity effect is not the only weak effect that interests us,” says Andrejevic. “The machine learning framework we developed can be easily applied to different types of problems, such as the superconducting proximity effect, which is of great interest in research[{” attribute=””>quantum computing.”

Reference: “Elucidating proximity magnetism through polarized neutron reflectometry and machine learning” by Nina Andrejevic, Zhantao Chen, Thanh Nguyen, Leon Fan, Henry Heiberger, Ling-Jie Zhou, Yi-Fan Zhao, Cui-Zu Chang, Alexander Grutter and Mingda Li, 17 March 2022, Applied Physics Review.
DOI: 10.1063/5.0078814

This research was funded by the U.S. Department of Energy Office of Science’s Neutron Scattering Program.

Photo Credit: Image by Ellen Weiss/Argonne National Laboratory

Magnetic interactions could point to quantum devices.

From MRI machines to computer hard drive storage, magnetism has played a role in crucial discoveries reshaping our society. In the new area of[{” attribute=””>quantum computing, magnetic interactions could play a role in relaying quantum information.

In new research from the U.S. Department of Energy’s (DOE) Argonne National Laboratory, scientists have achieved efficient quantum coupling between two distant magnetic devices, which can host a certain type of magnetic excitations called magnons. These excitations happen when an electric current generates a magnetic field. Coupling allows magnons to exchange energy and information. This kind of coupling may be useful for creating new quantum information technology devices.

“Remote coupling of magnons is the first step, or almost a prerequisite, for doing quantum work with magnetic systems,” said Argonne senior scientist Valentine Novosad, an author of the study. “We show the ability for these magnons to communicate instantly with each other at a distance.”

This instant communication does not require sending a message between magnons limited by the speed of light. It is analogous to what physicists call quantum entanglement.

Schematic of the remote magnon-magnon coupling circuit. Two single-crystal YIG spheres are embedded to the NbN coplanar superconducting resonator circuit, where microwave photon mediates coherent magnon-magnon interaction. Credit: Image by Yi Li/Argonne National Laboratory

Following on from a 2019 study, the researchers sought to create a system that would allow magnetic excitations to talk to one another at a distance in a superconducting circuit. This would allow the magnons to potentially form the basis of a type of quantum computer. For the basic underpinnings of a viable quantum computer, researchers need the particles to be coupled and stay coupled for a long time.

In order to achieve a strong coupling effect, researchers have built a superconducting circuit and used two small yttrium iron garnet (YIG) magnetic spheres embedded on the circuit. This material, which supports magnonic excitations, ensures efficient and low-loss coupling for the magnetic spheres.

The two spheres are both magnetically coupled to a shared superconducting resonator in the circuit, which acts like a telephone line to create strong coupling between the two spheres even when they are almost a centimeter away from each other – 30 times the distance of their diameters.

“This is a significant achievement,” said Argonne materials scientist Yi Li, lead author of the study. “Similar effects can also be observed between magnons and superconducting resonators, but this time we did it between two magnon resonators without direct interaction. The coupling comes from indirect interaction between the two spheres and the shared superconducting resonator.”

One additional improvement over the 2019 study involved the longer coherence of the magnons in the magnetic resonator. “If you speak in a cave, you may hear an echo,” said Novosad. “The longer that echo lasts, the longer the coherence.”

“Before, we definitely saw a relationship between magnons and a superconducting resonator, but in this study their coherence times are much longer because of the use of the spheres, which is why we can see evidence of separated magnons talking to each other,” Li added.

According to Li, because the magnetic spins are highly concentrated in the device, the study could point to miniaturizable quantum devices. “It’s possible that tiny magnets could hold the secret to new quantum computers,” he said.

The magnonic devices were fabricated at Argonne’s Center for Nanoscale Materials, a DOE Office of Science user facility.

A paper based on the study, “Coherent coupling of two remote magnonic resonators mediated by superconducting circuits,” was published in the January 24 issue of Physical Review Letters.

Reference: “Coherent Coupling of Two Remote Magnonic Resonators Mediated by Superconducting Circuits” by Yi Li, Volodymyr G. Yefremenko, Marharyta Lisovenko, Cody Trevillian, Tomas Polakovic, Thomas W. Cecil, Peter S. Barry, John Pearson, Ralu Divan, Vasyl Tyberkevych, Clarence L. Chang, Ulrich Welp, Wai-Kwong Kwok and Valentine Novosad, 24 January 2022, Physical Review Letters.
DOI: 10.1103/PhysRevLett.128.047701

Other authors of the study include Argonne’s Volodymyr Yefremenko, Marharyta Lisovenko, Tomas Polakovic, Thomas Cecil, Pete Barry, John Pearson, Ralu Divan, Clarence Chang, Ulrich Welp and Wai-Kwong Kwok. Cody Trevillian and Vasyl Tyberkevych of Oakland University in Michigan also contributed to the research.

The research was funded by DOE’s Office of Science (Office of Basic Energy Sciences).

A new technique could allow a robot to manipulate squishy objects like pizza dough or soft materials like clothing.

Imagine a pizza maker working with a ball of dough. She could use a spatula to lift the dough onto a cutting board, then use a rolling pin to flatten it into a circle. just right? Not when that pizza maker is a robot.

Working with a deformable object like dough is difficult for a robot because the shape of the dough can change in many ways that are difficult to represent with an equation. Also, creating a new shape from this dough requires multiple steps and the use of different tools. It is particularly difficult for a robot to learn a manipulation task with a long sequence of steps – where there are many choices – since learning is often done through trial and error.

researchers at[{” attribute=””>MIT, Carnegie Mellon University, and the University of California at San Diego, have come up with a better way. They created a framework for a robotic manipulation system that uses a two-stage learning process, which could enable a robot to perform complex dough-manipulation tasks over a long timeframe. A “teacher” algorithm solves each step the robot must take to complete the task. Then, it trains a “student” machine-learning model that learns abstract ideas about when and how to execute each skill it needs during the task, like using a rolling pin. With this knowledge, the system reasons about how to execute the skills to complete the entire task.

The researchers show that this method, which they call DiffSkill, can perform complex manipulation tasks in simulations, like cutting and spreading dough, or gathering pieces of dough from around a cutting board, while outperforming other machine-learning methods.

Researchers from MIT and elsewhere have created a framework that could enable a robot to effectively complete complex manipulation tasks with deformable objects, like dough or cloth, that require many tools and take a long time to complete. Credit: Images courtesy of the researchers

Beyond pizza-making, this method could be applied in other settings where a robot needs to manipulate deformable objects, such as a caregiving robot that feeds, bathes, or dresses someone elderly or with motor impairments.

“This method is closer to how we as humans plan our actions. When a human does a long-horizon task, we are not writing down all the details. We have a higher-level planner that roughly tells us what the stages are and some of the intermediate goals we need to achieve along the way, and then we execute them,” says Yunzhu Li, a graduate student in the Computer Science and Artificial Intelligence Laboratory (CSAIL), and author of a paper presenting DiffSkill.

Li’s co-authors include lead author Xingyu Lin, a graduate student at Carnegie Mellon University (CMU); Zhiao Huang, a graduate student at the University of California at San Diego; Joshua B. Tenenbaum, the Paul E. Newton Career Development Professor of Cognitive Science and Computation in the Department of Brain and Cognitive Sciences at MIT and a member of CSAIL; David Held, an assistant professor at CMU; and senior author Chuang Gan, a research scientist at the MIT-IBM Watson AI Lab. The research will be presented at the International Conference on Learning Representations.

Student and teacher

The “teacher” in the DiffSkill framework is a trajectory optimization algorithm that can solve short-horizon tasks, where an object’s initial state and target location are close together. The trajectory optimizer works in a simulator that models the physics of the real world (known as a differentiable physics simulator, which puts the “Diff” in “DiffSkill”). The “teacher” algorithm uses the information in the simulator to learn how the dough must move at each stage, one at a time, and then outputs those trajectories.

Then the “student” neural network learns to imitate the actions of the teacher. As inputs, it uses two camera images, one showing the dough in its current state and another showing the dough at the end of the task. The neural network generates a high-level plan to determine how to link different skills to reach the goal. It then generates specific, short-horizon trajectories for each skill and sends commands directly to the tools.

The researchers used this technique to experiment with three different simulated dough-manipulation tasks. In one task, the robot uses a spatula to lift dough onto a cutting board then uses a rolling pin to flatten it. In another, the robot uses a gripper to gather dough from all over the counter, places it on a spatula, and transfers it to a cutting board. In the third task, the robot cuts a pile of dough in half using a knife and then uses a gripper to transport each piece to different locations.

Researchers developed a robotic manipulation system can perform complex dough manipulation tasks with tools in simulations, like gathering dough and placing it onto a cutting board (left), cutting a piece of dough in half and separating the halves (center), and lifting dough onto a cutting board then flattening it with a rolling pin (right). Their technique is able to perform these tasks successfully, while other machine learning methods fail. Credit: MIT

A cut above the rest

DiffSkill was able to outperform popular techniques that rely on reinforcement learning, where a robot learns a task through trial and error. In fact, DiffSkill was the only method that was able to successfully complete all three dough manipulation tasks. Interestingly, the researchers found that the “student” neural network was even able to outperform the “teacher” algorithm, Lin says.

“Our framework provides a novel way for robots to acquire new skills. These skills can then be chained to solve more complex tasks which are beyond the capability of previous robot systems,” says Lin.

Because their method focuses on controlling the tools (spatula, knife, rolling pin, etc.) it could be applied to different robots, but only if they use the specific tools the researchers defined. In the future, they plan to integrate the shape of a tool into the reasoning of the “student” network so it could be applied to other equipment.

The researchers intend to improve the performance of DiffSkill by using 3D data as inputs, instead of images that can be difficult to transfer from simulation to the real world. They also want to make the neural network planning process more efficient and collect more diverse training data to enhance DiffSkill’s ability to generalize to new situations. In the long run, they hope to apply DiffSkill to more diverse tasks, including cloth manipulation.

Reference: “Diffskill: Skill Abstraction From Differentiable Physics for Deformable Object Manipulations With Tools” by Xingyu Lin, Zhiao Huang, Yunzhu Li, Joshua B. Tenenbaum, David Held and Chuang Gan.
OpenReview

This work is supported, in part, by the National Science Foundation, LG Electronics, the MIT-IBM Watson AI Lab, the Office of Naval Research, and the Defense Advanced Research Projects Agency Machine Common Sense program.