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.

A new method of making carbon fiber could turn refinery by-products into high-quality, ultra-lightweight structural materials for cars, planes and spacecraft.

As the world struggles to improve the efficiency of cars and other vehicles to reduce greenhouse gas emissions and increase the range of electric vehicles, the search is on for ever lighter materials that are strong enough to be used in car bodies.

Lightweight carbon fiber materials, similar to the material used in some tennis racquets and bicycles, combine exceptional strength with low weight, but were more expensive to manufacture than comparable steel or aluminum structural members. Well, researchers at[{” attribute=””>MIT and elsewhere have come up with a way of making these lightweight fibers out of an ultracheap feedstock: the heavy, gloppy waste material left over from the refining of petroleum, material that refineries today supply for low-value applications such as asphalt, or eventually treat as waste.

Not only is the new carbon fiber cheap to make, but it offers advantages over the traditional carbon fiber materials because it can have compressional strength, meaning it could be used for load-bearing applications. The new process is described on March 18, 2022m in the journal Science Advances, in a paper by graduate student Asmita Jana, research scientist Nicola Ferralis, professor Jeffrey Grossman, and five others at MIT, Western Research Institute in Wyoming, and Oak Ridge National Laboratory in Tennessee.

A circle of carbon fibers which have a diameter of about 10 micrometers. Credit: Nicola Ferralis

The research began about four years ago in response to a request from the Department of Energy, which was seeking ways to make cars more efficient and reduce fuel consumption by lowering their overall weight. “If you look at the same model car now, compared to 30 years ago, it’s significantly heavier,” Ferralis says. “The weight of cars has increased more than 15 percent within the same category.”

A heavier car requires a bigger engine, stronger brakes, and so on, so the reducing the weight of the body or other components has a ripple effect that produces additional weight savings. The DOE is pushing for the development of lightweight structural materials that match the safety of today’s conventional steel panels but also can be made cheaply enough to potentially replace steel altogether in standard vehicles.

Composites made from carbon fibers are not a new idea, but so far in the automotive world they have only been used in a few very expensive models. The new research aims to turn that around by providing a low-cost starting material and relatively simple processing methods.

A human hair and carbon fiber, with a clear ruler on the bottom half of the image. The human hair, pictured in a vertical orientation, is thicker (about 60 micrometers) than the carbon fiber behind it. Credit: Nicola Ferralis

Carbon fibers of the quality needed for automotive use cost at least $10 to $12 per pound currently, Ferralis says, and “can be way more,” up to hundreds of dollars a pound for specialized application like spacecraft components. That compares to about 75 cents a pound for steel, or $2 for aluminum, though these prices fluctuate widely, and the materials often rely on foreign sources. At those prices, he says, making a pickup truck out of carbon fiber instead of steel would roughly double the cost.

These fibers are typically made from polymers (such as polyacrilonitrile) derived from petroleum, but use a costly intermediate step of polymerizing the carbon compounds. The cost of the polymer can account for more than 60 percent of the total cost of the final fiber, Ferralis says. Instead of using a refined and processed petroleum product to start with, the team’s new approach uses what is essentially the dregs left after the refining process, a material known as petroleum pitch. “It’s what we sometimes call the bottom of the barrel,” Ferralis says.

“Pitch is incredibly messy,” he says. It’s a hodgepodge of mixed heavy hydrocarbons, and “that’s actually what makes it beautiful in a way, because there’s so much chemistry that can be exploited. That makes it a fascinating material to start with.”

It’s useless for combustion; although it can burn, it’s too dirty a fuel be practical, and this is especially true with tightening environmental regulations. “There’s so much of it,” he says, “the inherent value of these products is very low, so then it is often landfilled.” An alternative source of pitch, which the team also tested, is coal pitch, a similar material that is a byproduct of coking coal, used for example for steel production. That process yields about 80 percent coke and 20 percent coal pitch, “which is basically a waste,” he says.

Working in collaboration with researchers at Oak Ridge National Laboratory, who had the expertise in manufacturing carbon fibers under a variety of conditions, from lab scale all the way up to pilot-plant scale, the team set about finding ways to predict the performance in order to guide the choice of conditions for those fabrication experiments.

“The process that you need to actually make a carbon fiber [from pitch] is actually extremely small, both in terms of energy requirements and in terms of the actual processing that you need to do,” says Ferralis.

Jana explains that pitch “consists of this heterogeneous group of molecules where you would expect the properties to change dramatically as you change shape or size,” whereas an industrial material must have very consistent properties.

By carefully modeling how bonds form and crosslink between individual molecules, Jana was able to develop a way to predict how a given set of processing conditions would affect the resulting fiber properties. “We were able to reproduce the results so amazingly[{” attribute=””>accuracy,” she says, “to the point where companies could take those graphs and be able to predict” characteristics such as density and elastic modulus of the fibers.

The work produced results showing by adjusting the starting conditions, carbon fibers could be made that were not only strong in tension, as most such fibers are, but also strong in compression, meaning they could potentially be used in load-bearing applications. This opens up entirely new possibilities for the usefulness of these materials, they say.

DOE’s call was for projects to bring the cost of lightweight materials down below $5 a pound, but the MIT team estimates that their method can to do better than that, reaching something like $3 a pound, though they haven’t yet done a detailed economic analysis.

“The new route we’re developing is not just a cost effect,” Ferralis says. “It might open up new applications, and it doesn’t have to be vehicles.” Part of the complication of making conventional fiber composites is that the fibers have to be made into a cloth and laid out in precise, detailed patterns. The reason for that, he says, “is to compensate for the lack of compressive strength.” It’s a matter of engineering to overcome the deficiencies of the material, he says, but with the new process all that extra complexity would not be needed.

Reference: “Atoms to fibers: Identifying novel processing methods in the synthesis of pitch-based carbon fibers” by Asmita Jana, Taishan Zhu, Yanming Wang, Jeramie J. Adams, Logan T. Kearney, Amit K. Naskar, Jeffrey C. Grossman and Nicola Ferralis, 18 March 2022, Science Advances.
DOI: 10.1126/sciadv.abn1905

The research team included Taishan Zhu and Yanming Wang at MIT, Jeramie Adams at Western Reserve University, and Logan Kearney and Amit Naskar at Oak Ridge National Laboratory. The work was supported by the U.S. Department of Energy.

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.