Transistors, the building blocks of modern electronics, are typically made of silicon. Because it’s a semiconductor, this material can control the flow of electricity in a circuit. But silicon has fundamental physical limits that restrict how compact and energy-efficient a transistor can be.

MIT researchers have now replaced silicon with a magnetic semiconductor, creating a magnetic transistor that could enable smaller, faster, and more energy-efficient circuits. The material’s magnetism strongly influences its electronic behavior, leading to more efficient control of the flow of electricity. 

The team used a novel magnetic material and an optimization process that reduces the material’s defects, which boosts the transistor’s performance.

The material’s unique magnetic properties also allow for transistors with built-in memory, which would simplify circuit design and unlock new applications for high-performance electronics.

“People have known about magnets for thousands of years, but there are very limited ways to incorporate magnetism into electronics. We have shown a new way to efficiently utilize magnetism that opens up a lot of possibilities for future applications and research,” says Chung-Tao Chou, an MIT graduate student in the departments of Electrical Engineering and Computer Science (EECS) and Physics, and co-lead author of a paper on this advance.

Chou is joined on the paper by co-lead author Eugene Park, a graduate student in the Department of Materials Science and Engineering (DMSE); Julian Klein, a DMSE research scientist; Josep Ingla-Aynes, a postdoc in the MIT Plasma Science and Fusion Center; Jagadeesh S. Moodera, a senior research scientist in the Department of Physics; and senior authors Frances Ross, TDK Professor in DMSE; and Luqiao Liu, an associate professor in EECS, and a member of the Research Laboratory of Electronics; as well as others at the University of Chemistry and Technology in Prague. The paper appears today in Physical Review Letters.

Overcoming the limits

In an electronic device, silicon semiconductor transistors act like tiny light switches that turn a circuit on and off, or amplify weak signals in a communication system. They do this using a small input voltage.

But a fundamental physical limit of silicon semiconductors prevents a transistor from operating below a certain voltage, which hinders its energy efficiency.

To make more efficient electronics, researchers have spent decades working toward magnetic transistors that utilize electron spin to control the flow of electricity. Electron spin is a fundamental property that enables electrons to behave like tiny magnets.

So far, scientists have mostly been limited to using certain magnetic materials. These lack the favorable electronic properties of semiconductors, constraining device performance.

“In this work, we combine magnetism and semiconductor physics to realize useful spintronic devices,” Liu says.

The researchers replace the silicon in the surface layer of a transistor with chromium sulfur bromide, a two-dimensional material that acts as a magnetic semiconductor.

Due to the material’s structure, researchers can switch between two magnetic states very cleanly. This makes it ideal for use in a transistor that smoothly switches between “on” and “off.”

“One of the biggest challenges we faced was finding the right material. We tried many other materials that didn’t work,” Chou says.

They discovered that changing these magnetic states modifies the material’s electronic properties, enabling low-energy operation. And unlike many other 2D materials, chromium sulfur bromide remains stable in air.

To make a transistor, the researchers pattern electrodes onto a silicon substrate, then carefully align and transfer the 2D material on top. They use tape to pick up a tiny piece of material, only a few tens of nanometers thick, and place it onto the substrate.

“A lot of researchers will use solvents or glue to do the transfer, but transistors require a very clean surface. We eliminate all those risks by simplifying this step,” Chou says.

Leveraging magnetism

This lack of contamination enables their device to outperform existing magnetic transistors. Most others can only create a weak magnetic effect, changing the flow of current by a few percent or less. Their new transistor can switch or amplify the electric current by a factor of 10.

They use an external magnetic field to change the magnetic state of the material, switching the transistor using significantly less energy than would usually be required.

The material also allows them to control the magnetic states with electric current. This is important because engineers cannot apply magnetic fields to individual transistors in an electronic device. They need to control each one electrically.

The material’s magnetic properties could also enable transistors with built-in memory, simplifying the design of logic or memory circuits.

A typical memory device has a magnetic cell to store information and a transistor to read it out. Their method can combine both into one magnetic transistor.

“Now, not only are transistors turning on and off, they are also remembering information. And because we can switch the transistor with greater magnitude, the signal is much stronger so we can read out the information faster, and in a much more reliable way,” Liu says.

Building on this demonstration, the researchers plan to further study the use of electrical current to control the device. They are also working to make their method scalable so they can fabricate arrays of transistors.

This research was supported, in part, by the Semiconductor Research Corporation, the U.S. Defense Advanced Research Projects Agency (DARPA), the U.S. National Science Foundation (NSF), the U.S. Department of Energy, the U.S. Army Research Office, and the Czech Ministry of Education, Youth, and Sports. The work was partially carried out at the MIT.nano facilities.

As people age, they may sleep for shorter periods and wake up more frequently during the night. In addition to those disruptions, MIT researchers have now discovered that a process responsible for flushing waste from the brain during sleep is commonly impaired in older people.

The researchers found that waves of cerebrospinal fluid (CSF), which help remove debris from the brain, are significantly reduced in older adults. This impairment is linked to a decrease in the slow brain waves that normally occur during deep sleep, the researchers found, and is not just a result of getting less sleep.

The researchers now hope to explore whether the disruption of CSF flow increases the risk of neurodegenerative disorders, and if any of the sleep patterns they observed might offer a way to predict an individual’s risk.

“We know that people age really differently. Some people age very successfully with great cognitive outcomes, and other people develop cognitive decline. If we can figure out who has great trajectories of aging and what are the factors that are associated with that, we might be able to use that to predict future outcomes,” says Laura Lewis, the Athinoula A. Martinos Associate Professor of Electrical Engineering and Computer Science, a member of MIT’s Institute for Medical Engineering and Science and the Research Laboratory of Electronics, and an associate member of the Picower Institute for Learning and Memory.

Her lab also plans to investigate whether there might be any interventions that could improve CSF flow in older adults.

Lewis is the senior author of the study, which appears today in Nature Aging. Sydney Bailes, a postdoc in the MIT Laboratory for Information and Decision Systems, and Stephanie Williams, a former visiting student at MIT who earned her PhD from Boston University in 2026, are the lead authors of the paper.

Smaller waves

During the deepest stages of sleep, also called non-REM sleep, high-amplitude, low-frequency waves contribute to physical recovery, memory consolidation, and many other functions. In a 2019 study, Lewis found that these slow waves are also critical for generating waves of CSF that help flush waste from the brain.

That finding led her to wonder whether reductions in slow waves in older people might also reduce CSF flow. Previous studies of aging have yielded conflicting evidence; some found that CSF flow goes down in older adults, while others found it goes up, but these studies did not monitor flow during sleep.

In the latest study, the researchers recruited volunteers in two age groups: 18 to 39 and 60 to 84. The tests lasted for about two hours and began late at night, so that participants would be sleepy. 

The researchers used traditional fMRI to measure participants’ blood flow in the brain, along with a modified MRI technique, developed by Lewis, that measures CSF flow. While the subjects were in the scanner, the researchers also measured their brain waves using EEG. 

Many of the participants fell asleep in the scanner, allowing the researchers to compare their brain activity while awake and while sleeping. 

As they had seen in earlier work, the researchers found that when the younger adults fell into non-REM sleep, their EEG slow waves became stronger, and their CSF waves became larger. However, in the older group, the average CSF waves were significantly smaller than those seen in the younger group. 

Impaired flow

In additional experiments done while subjects were fully awake, the researchers tried to figure out what was causing the age-related declines in CSF flow.

First, they explored the possibility that changes in a phenomenon known as neurovascular coupling might play a role. When localized brain activity increases the demand for oxygen, nearby blood vessels dilate and deliver more oxygen and nutrients. That increase in blood flow can be measured with fMRI.

The researchers tested each subject’s neurovascular coupling by measuring blood flow while the subjects looked at a flickering checkerboard image — a task that typically drives an increase in neural activity, along with an increase in blood volume.

In many of the older subjects, while their neural activity did go up, the blood flow didn’t increase much, suggesting that the blood vessels’ ability to respond to increased oxygen demand has diminished.

During another task, the researchers tested whether the brain’s vascular system might be impaired in the older subjects. They asked the subjects to hold their breath, then measured what happened inside the brain. Normally, this leads to an increase in carbon dioxide levels in the blood, which triggers an increase in cerebral blood flow. However, in the older participants, the average blood flow increase was smaller than the increase seen in the younger subjects.

These two factors — reduced neurovascular coupling and diminished blood flow — accounted for much of the decline in CSF flow but could not explain all of it, according to a statistical model developed by the researchers. This suggests that there may be other age-related factors that contribute to the reduction in CSF flow, they say.

“Aging is so complex, and so many different things change, that it’s really hard to disentangle the individual factors,” Bailes says. “We try to account for some of the mechanisms, but there definitely are a lot of other factors related to vascular health and lifestyle. If you were able to measure all of them, you might be able to break it apart into different explanations for why an individual’s CSF flow might be more or less impaired.” 

Enhancing CSF flow

Previous studies have found that sleep disturbances are linked to a greater risk of neurodegenerative disorders such as Alzheimer’s and other forms of dementia.

None of the subjects in this study had been diagnosed with a neurodegenerative disorder, but the researchers found that some of the older adults showed signs of gray matter atrophy, which was correlated with a greater decrease in CSF flow. They hope to continue studying some of the participants in hopes of discovering whether changes to CSF flow are associated with a higher risk of developing a neurodegenerative disorder.

They also hope to test possible interventions that might help increase CSF flow. In a recent study, Lewis’ lab found that short bursts of “pink noise” during sleep could increase CSF flow in younger adults, but they have not yet tested that intervention in older adults. 

In other work, they plan to study why some older adults don’t experience a dropoff in CSF flow. In this study, the researchers found that while as a group, older adults showed weaker CSF waves during sleep, some individuals in the group maintained normal CSF waves.

“It’s really intriguing that for some of these older adults, their CSF fell right in the range of the healthy young adults,” Lewis says. “We are really interested in what’s different in those people and how that might be related to future health outcomes.”

The research was funded by the National Institutes of Health, the Simons Collaboration on Plasticity in the Aging Brain, a McKnight Scholar Award, a Pew Biomedical Scholar Award, an MIT EECS Transformative Research Award, the Picower Institute Neurological Disorder Discovery Research Fund, a Sloan Fellowship, and the Department of Defense National Defense Science and Engineering Graduate Fellowship Program.

The use of algorithms to design structures has led to new parts optimized for ultralight aircraft, high-bandwidth antennas, and F1 racing cars.

But the best algorithmic design techniques still require lots of computation and time: Users only see the final design after it’s complete, after which they often must start the design process all over again to make changes. That process also makes it harder to trust that the final designs are structurally sound.

MIT researchers are making algorithmic designs more collaborative with a system that acts like an AI copilot. As the algorithm is generating the structure, it presents regions to the human designer for feedback.

In an open-access Communications Engineering paper describing the system, the researchers showed it led to more easily manufacturable structures with higher buckling loads while adding only 15 seconds to the total design time compared to all-algorithmic approaches.

The researchers believe the approach, which uses a design technique known as topology optimization, could make algorithmic design more accessible and manufacturable for a broader class of design engineers.

“Right now, the human doesn’t have any agency or control over the design process as it’s playing out,” explains lead author Dat Ha PhD ’26, a recent MIT civil and environmental engineering doctoral student who is now a structural designer at LeMessurier. “For these really complex designs, we wanted to also tap into our brain, which is the most complex machine there is, to solve these design problems in a way that’s more practical to industry.”

Joining Ha on the paper are Ferdous Alam, a former MIT postdoc who is now an assistant professor at Georgia Tech; Markus Buehler, the MIT Jerry McAfee (1940) Professor of Engineering; Faez Ahmed, MIT associate professor of mechanical engineering; and senior author Josephine Carstensen, MIT associate professor of civil and environmental engineering.

Bringing a human in the loop

Topology optimization is one of the most advanced and promising algorithmic design techniques. It uses formal optimization techniques to iteratively fill in the space given for a design in a way that maximally achieves the stated goals. For most applications, the process requires a lot of computation and time. When it’s finally done, the designs can be impractical to build or have other issues the engineer didn’t consider. For instance, some designs might have eight or more beam connections at a single point, which would be difficult to make in the real world.

“Engineers are always very skeptical of computer programs, especially if they give you very weird-looking or unexpected designs,” Ha says. “On paper, topology optimization is really powerful and can achieve great, high-performing designs, but its black box nature has made it less widely adopted in industry.”

Topology optimization has gotten traction in the automotive and aerospace industries, but it’s been used less to design buildings or bridges, partly because design engineers need to know they can trust the output.

“A big limiting factor has been that the design engineer has very little influence over how the design evolves and therefore can’t control undesirable features,” Carstensen explains. “The lack of control and lack of ownership also inhibits its use.”

To address those problems, the researchers made topology optimization more collaborative in an approach that Carstensen compares to having a design copilot.

Prior attempts at adding human judgment slowed things down dramatically. The researchers’ new system uses machine learning to quickly identify the most likely regions of an algorithmic design that a human would want to change. To train the machine-learning model, the researchers created a synthetic dataset of probable human feedback to thousands of designs that critiqued things like aesthetics, performance criteria, and manufacturability.

“We thought if we could create a record of where humans have made changes to these programs, then perhaps there’s a pattern to those choices, which is not very different from other types of machine-learning approaches,” Ha says.

Carstensen sees the system as bringing together the best of algorithmic and human design approaches.

“The idea is to tap into the brains of the designers and get them to infuse their domain knowledge as the design evolves,” Carstensen says.

In demonstrations, the researchers showed their human-in-the-loop approach improved manufacturability and linear buckling load by 39 percent while only modestly increasing the total design time.

Right now, the approach only allows users to make structures within identified regions thicker or thinner, but they hope to add other options for modification in future versions.

“The hope is that by addressing these hurdles, we open up topology optimization to a much wider audience, not only in industry but also to researchers and students in academia,” Carstensen says.

From designs to structures

The researchers see their approach complementing AI only-approaches to design.

“We are arguing there’s room for both,” Carstensen says. “If you are in a discipline that has a lot of computational resources, you can use the other approach. But if you really need the human input, you can use this collaborative approach.”

Adding human input also serves to increase trust.

“In some settings, the humans need to make the final decisions before advancing the final design,” Carstensen says. “By involving humans, we can get higher-performing designs to the real world to create higher-performing structures. Because it’s not just about designing good artifacts; it’s about creating good artifacts that humans will actually accept and make.”

The work was supported, in part, by the MathWorks Engineering Fellowship Fund, TATA Steel, and the MIT Generative AI Impact Consortium.

Despite fears about robots replacing humans at work, there are still plenty of things humans can do far better than machines. That’s fine with Robust AI, a startup building robots designed to complement what humans do best.

The company’s mobile robot, named Carter, resembles a utility cart on wheels and is designed to fit seamlessly alongside humans in fast-changing warehouse and factory environments. Carter features a force-sensitive handlebar for easy guidance, an AI-powered vision system, and holonomic wheels for instant movement in any direction.

As a result, humans can cut Carter off, push and pull it in any direction with one hand, and direct it with gestures or the onboard touch screen.

The company’s robots are currently deployed in logistics warehouses and on factory floors, where they help humans while picking or stocking shelves and moving items across facilities autonomously.

“In these environments, what really determines success is how the overall system performs,” explains Anthony Jules ’92, SM ’92, who co-founded the company with MIT Professor Emeritus Rodney Brooks. “The robot does some of the work, the people do some of the work, and the backend systems do some of the work. It’s that combination that determines how well your system performs. We believe it’s really important that people have an appropriate level of agency and input so that they’re always moving the system forward.”

Collaborative robots

Brooks was a faculty member at MIT from 1984 until 2010 and served as the director of the Computer Science and Artificial Intelligence Laboratory (CSAIL) from 1997 until 2007. While at MIT, he co-founded iRobot, which invented the popular Roomba vacuum and went public in 2005. Later, he co-founded the company Rethink Robotics, which also designed collaborative robots.

Jules met Brooks as a graduate student at CSAIL in the 1990s. The two never worked together, but their labs were on the same floor. The pair kept in touch after Jules graduated and co-founded the company Sapient, which went public before being acquired by Publicis in 2015.

In 2019, Brooks bumped into Jules at a robotics conference. He told Jules he was thinking of starting a robotics software company, which he compared to a video game engine for training robots. Robust AI was founded later that year.

The Covid-19 pandemic caused the founders to shift to creating robots that could autonomously disinfect surfaces, but when it was discovered that Covid-19 was not transmitted via surfaces, they had to pivot again.

“That forced us to decide how to use this technology we had been building, which was really about helping robots understand the world more fully,” Jules recalls. “Long story short, I said to Rodney, ‘I have an idea. You’re going to love it and you’re going to hate it. You’re going to love it because it’s solving problems for customers that we’ve been hearing about, and you’re going to hate it because we have to build hardware.”

The founders had been interviewing potential customers for years through Robust AI’s early pivots. They finally decided on logistics and manufacturing as the industries to target early on.

“When people think about automation in factories, they think about pictures of the 5 percent that are fully automated with $50 to $200 million dollar systems,” says Jules, who now serves as Robust AI’s chief innovation officer. “They don’t think about the vast majority of factories that aren’t automated at all. These factories don’t even have a conveyor belt. They are not going to tear everything out and replace it with a $200 million system.”

Robust AI’s robots can be phased into operations as needed and offer several workflows for customers. In one workflow, the robot can guide human workers to specific spaces to retrieve items. In another it can follow humans to important spots for loading. The primary workflows involve Carter robots moving things from one place to another, often in large warehouses or factories, where they say human workers can walk on average 30,000 steps a day.

“These are huge spaces, so a person might load the robot with things and then need to bring them to another station, but that could be 400 feet away,” says, Brooks, Robust AI’s chief technology officer. “Now the person can just say ‘done’ and the robot brings it instead. How the robots are integrated into the workspace is entirely up to the customer.”

Getting work done

Leila Takayama, Robust AI’s vice president of human robot interaction, recently discovered a peer-reviewed study of robots in hospitals, which were used to do things like gather dishes, move sheets, and transport other equipment. The researchers discovered that, in many hospitals, a large number of robots were turned off and against the walls. The nurses said they broke.

“What really happened was the nurses were rushing with a patient on a gurney, and the robot sees them in the hallway and stops dead, and they couldn’t get the gurney around them,” Brooks says. “The robots were getting in the way of nurses’ primary tasks.”

Robust AI’s approach to technology development is to understand workers’ primary tasks deeply and build features that accelerate those tasks, whether a human or a robot is doing them.

“We let humans do what humans are good at in the physical world, which we’ve had millions of years of evolution to get good at, and have the digital system respond to that and provide us with all the things that it is good at,” Brooks explains.

As humans expand their ambitions in deep space, energy-intensive missions can’t afford to rely solely on combustion or solar power. They will need nuclear energy for propulsion and surface habitation.

Jake Hecla ’17, who holds a dual appointment as assistant professor in the Department of Nuclear Science and Engineering (NSE) and Charles Stark Draper Career Development Professor of Aeronautics and Astronautics (AeroAstro), is researching ways to address one of the principal heat transfer challenges in such deployments of nuclear power in space: a lack of atmosphere. He is also working on technologies to enable the deployment of aerospace reactors, observe their operational signatures from long range, and create the technical basis for future arms-control agreements limiting the weaponization of these systems.

Thermal management in space

The next generation of space nuclear reactors demand considerably higher power than past missions, requiring the conversion of heat to power, typically using a Brayton or Rankine cycle. Such conversions deliver efficiencies of around 30-40 percent in terrestrial applications — and a lot of heat that needs to be removed. Because there is no atmosphere to rely on for convection, engineers use radiators instead. But traditional solid metal or composite radiator panels are mass-intensive, a significant impediment for space applications.

One of Hecla’s research projects at MIT addresses the limitations of an alternate solution: the liquid droplet radiator (LDR). First proposed by researchers at the University of Washington in the late 1970s, the LDR could be just as effective as conventional radiators, especially because their considerably lower weight is an advantage, helping radiators constitute just a fraction of the spacecraft’s mass.

In place of solid panels, LDRs use sheets of liquid droplets to radiate heat away. The LDR works by spraying coolant from an emitter to a collector as sheets of tiny droplets. As these droplets travel through space, they radiate and cool. They then impact a collector, which then pumps the coolant back to the heat source in a cyclical operation.

The principle sounds straightforward, but droplet retrieval is often complicated by drifts introduced by charging, splashing at the collector, and emitter misalignment. Engineers can’t afford to lose too many of them — acceptable loss rates could be one droplet in every million for a 10-year mission — because that will increase the coolant load that will need to be transported to space. (The rate might be small, but the radiator cycles the coolant every few seconds, meaning even a minuscule loss rate adds up over time.) Hecla is investigating a variety of collection techniques including electromagnetic collectors that can improve retrieval efficiency, and even allow maneuverability while the radiator is in operation.

If successful, this could reduce the mass of the cooling system for a future nuclear electric power space reactor by an order of magnitude. The work will vastly improve the potential of nuclear power in space and potentially expand its utility to include low-Earth orbit projects as well.

An early interest in radiation detection — at MIT

With his appointment as an assistant professor, Hecla’s career has come full circle since his undergraduate days as a student at NSE.

Early on, he was especially interested in radiation detection techniques because of their use in many practical applications.

That specific interest was seeded during a formative childhood trip to New Mexico, which included a visit to a shop selling rocks and minerals. Hecla remembers being fascinated by the goods, which included uranium ore. He was hooked. Hecla researched the field and bought and built a do-it-yourself Geiger counter. “Throughout high school I was buying and repairing radiation equipment in my home. I collected high-voltage equipment, built a homemade X-ray system, and it was all downhill from there,” Hecla says.

That passion for measuring radiation spilled over to MIT. One of Hecla’s undergraduate research projects involved developing a prototype of a photocathode-driven X-ray unit, in collaboration with Massachusetts General Hospital. The research team he worked with formed a startup called Advanced Tomography Systems. As part of another undergraduate research opportunities program (UROP), Hecla worked with Areg Danagoulian, associate professor at NSE, on a novel approach for treaty verification.

A visit to Chernobyl

While at MIT, another formative event would cement Hecla’s deep commitment to measuring radiation: a friend, Carl Willis, now a professor at the University of New Mexico, invited Hecla to travel to Chernobyl. “It was to better understand the realities of living and working in a contaminated environment,” Hecla says.

The experience shook him — and he visited several times more during his undergraduate years. Since 2017, Hecla has served as scientific adviser to Clean Futures Fund, a U.S.-based nonprofit that ensures activities in the exclusion zone and at the power plant are as well supplied as they can be, providing basic essentials, personal protective equipment, and detection equipment.

“Chernobyl has a lot to teach us, both about the necessity of a dedication to safety in nuclear power and about managing the consequences of radiation release,” Hecla says. “It’s an enormously challenging environment where a small amount of well-informed work can have an outsized impact.”

Chernobyl also provided the context for Hecla’s graduate studies at the University of California at Berkeley, where he studied radiation imaging techniques. For his master’s, the goal was to use a free-moving radiation detector to reconstruct a three-dimensional image that would inform workers about radiation intensity so they could be judicious about exposure.

Another area of radiation detection, to detect antineutrinos as an indicator of nuclear fission, anchored his doctoral research — Hecla’s PhD dissertation was titled “Optical Diagnostics for Antineutrino Detector Media” — advised by Kai Vetter. The goal was to use the particles as an essentially unshieldable signature of reactor operation.

Guarding against the misuse of nuclear energy

As Hecla’s graduate research showed, using radiation signatures is an effective way to track when nuclear energy is being misused.

Hecla’s work on aerospace applications of nuclear power extends to both improving the viability of space reactors and improving our ability to monitor orbital and sub-orbital applications of nuclear energy.

His current research focuses on understanding and tracking air-breathing nuclear propulsion, which is a technique in which heat from a reactor is used for propulsion. Hecla became especially interested in the project when, in 2018, the Russian government announced that they were developing a nuclear-powered, nuclear-armed missile, the 9M730 Burevestnik, that would pose a direct security threat to Europe.

The missile was said to be powered by an air-breathing nuclear engine, enabling extreme duration flight. In 2019, a mishap with the system killed at least five scientists, and generated a plume of radionuclides that drifted across the White Sea, indirectly confirming that the Russians were serious about developing the missile.

Between 2023-25, as a Stanton Postdoctoral Fellow at MIT’s Laboratory for Nuclear Security and Policy, working with R. Scott Kemp, Hecla developed techniques through which he could understand the performance envelope and flight signatures of the nuclear propulsion system in the missile. That information could be used to limit or ban use of such military systems, or contribute to new countermeasures.

Along with colleagues at MIT, Hecla is devising techniques to detect the presence of nuclear weapons by studying the gamma-induced neutral field from satellites, which can share information about the presence of fissile material. Such a system is especially relevant given the Kosmos 2553, a satellite launched by Russia in 2022 that is believed by American intelligence to contain components of a nuclear weapon. 

The threat of nuclear weapons in space is immense. “Someone may find it advantageous to destroy most assets in low and medium Earth orbit if it gives them an edge during combat,” Hecla says. As part of this work, researchers are looking to develop a highly sensitive compact neutron telescope to detect and locate the source of neutrons as a way to detect nuclear activity.

Most of Hecla’s work, including the space thermal management projects, sits at the intersection of nuclear and the aerospace or aeronautical fields.

Hecla is delighted to be back at MIT where it all started. “We have the right combination of talent here that is strong and growing,” he says. “And between world-class facilities like the MITR-II (MIT Nuclear Research Reactor), the PSFC (Plasma Science Fusion Center), the people, this is absolutely the best place for research,” Hecla says.

The MIT Global Seed Funds (GSF) are designed to do more than launch research projects — they build international collaborations that make ambitious science possible. By supporting early-stage collaborations with researchers around the world, the program helps faculty test new ideas, exchange expertise, and lay the groundwork for discoveries that often grow far beyond the initial award. 

Matt Shoulders, professor of chemistry and department head, has experienced that impact firsthand. Since his first GSF grant in 2013, the collaborations these grants enabled have led to major research programs in protein engineering, photosynthesis, collagen biology, and gene-editing technologies, as well as subsequent funding. 

The GSF help Institute faculty and principle investigators create exciting new connections with colleagues abroad. GSF supports early-stage, open and publishable research collaborations with researchers at academic universities and public research institutions around the world. Administered by the Center for International Studies, GSF has awarded more than $30 million to over 1,300 faculty research projects since its inception in 2008. The current call for proposals from MIT faculty and researchers with principal investigator status is open until Dec. 3. 

Q: What makes the Global Seed Funds unique?

A: This has to be one of the standout ways to develop international collaborations that any school has anywhere. It's an extraordinary way to build serious relationships with people who have science that you can work on together, get to synergistic and exciting outcomes, and start projects that you could never get going otherwise.

I've found it to be an enormously valuable bridge-building mechanism for creating opportunities to work with other groups.

I started as an assistant professor in 2012 and received my first Global Seed Funds grant in 2013, probably within eight months of starting. That opportunity let me get to know an expert whose field of research was exactly what we needed. We published only one paper together, but that collaboration led directly to my first major NIH [National Institutes of Health] funding — a Director's New Innovator Award that brought nearly more than $2 million to the lab. It became the basis of what's still one of our lab's major research projects.

We never would have built that relationship otherwise. We visited their lab, they visited ours, our students exchanged visits, and together we built a whole platform for protein engineering that we still use all the time. Fourteen years later, that first GSF grant is still shaping the work we do.

That story has repeated itself over and over again. The collaborations have led us in lots of directions and created opportunities for different science along the way. Looking back, I think the GSF funding we've received can be directly connected to roughly $18 to $20 million in outside research funding. Of course, it wasn't only the GSF grants, but without them much of that later funding probably wouldn't have happened.

Q: Can you share a few examples of research that grew out of those collaborations?

A: One project is about engineering photosynthesis. The world needs more food, the climate is warming, fertilizer is becoming harder to obtain, water is increasingly scarce, and yet we need to grow more crops than ever. We need step-function improvements in agriculture, not just gradual ones.

We've focused on an enzyme called Rubisco, which takes carbon dioxide from the air and turns it into the sugars that build plants — and ultimately, all of us. Improving Rubisco has been a dream in agriculture for decades, but it's also been viewed as almost impossible.

One of the first pieces of funding we received for this project was a Global Seed Funds grant that allowed us to build a collaboration with a lab in Australia. They had technical assay expertise that existed in only a handful of laboratories worldwide. They came here, we went there, and we brought those capabilities to MIT. Now MIT is one of the few places that can perform those assays, and they've become part of the engine behind our entire Rubisco project.

That collaboration has since led to major foundation funding, publications, patents, and a great deal of exciting science. Whether we ultimately succeed in making Rubisco in a plant significantly better or not, we've already learned an enormous amount.

Another example involves collagen, the most abundant protein in the human body. There are dozens of diseases caused by collagen dysfunction, and we still can't treat them.

I met a researcher from Australia at a conference, and we realized our expertise complemented each other. The Global Seed Funds gave us the opportunity to build that collaboration. Since then, we received funding from the Mathers Foundation and multiple NIH grants. Today we've developed cartilage organoids that model these diseases, allowing us to study them and screen potential drug therapies. That entire research program exists because we were able to build that partnership.

The third example goes back to my very first Global Seed Funds grant. We wanted to develop a new way of doing directed evolution inside human cells, rather than bacteria. We had the idea, but we didn't have the adenovirus expertise to make it work.

That GSF-enabled collaboration gave us exactly the knowledge we needed. We recently published the first paper demonstrating CRISPR-Cas9 evolution inside human cells using the resulting platform. That work traces directly back to the GSF grant we received in 2013.

Q: How have MIT students benefited from these collaborations?

A: Most of the projects have involved graduate students, and they've really been the biggest beneficiaries. The collaborations often become the basis for their papers and dissertations.

When we've had the right undergraduate with the right interests, it's also been tremendously valuable, because they can spend an entire summer abroad supported by MISTI [MIT International Science and Technology Initiatives]. Michelle Huang is probably the flagship example. She spent several months in Australia, learning techniques that she brought back to MIT. She's a superstar who has now earned her PhD from Stanford, supported by an NSF [National Science Foundation] Graduate Fellowship awarded during her senior year at MIT.

Another great example is Julie McDonald, a superb MIT biology graduate student who worked on our Rubisco collaboration in Australia. She helped get that project going and has been tremendously successful. She's now a postdoctoral researcher at Rockefeller.

For our graduate students, these collaborations have been scientifically invaluable because they directly led to publications and launched major parts of their research careers.

Q: Have there been benefits beyond the research itself?

A: There are definitely personal benefits. Both the students and I have had opportunities to spend time in countries we otherwise wouldn't have visited. We've been exposed to different cultures and different ways of approaching science. India was a particularly memorable experience for me, in that respect.

I also think building partnerships with researchers in other countries has value beyond any individual project. On the scale of the Global Seed Funds program as a whole, strengthening those international relationships really matters.

Q: What would you tell other faculty members who are considering applying for Global Seed Funds?

A: If you've really got a plan and you're committed to building the collaboration, you should apply. The program can truly make the difference between an idea and a lasting research partnership. The resulting support can turn those partnerships — and the ideas behind them — into reality.

While rising global prosperity increases energy demand and resulting greenhouse gas emissions, promising low-carbon technologies offer reason for optimism. 

Eric Toone, Breakthrough Energy’s chief technical officer, gave this assessment in his keynote address at the recent Energizing@MIT, the annual research conference of the MIT Energy Initiative (MITEI).

“Energy is how we make things … build and heat our homes … grow and prepare our food. So, energy is prosperity,” said Toone.

Toone oversees the technological roadmap and evaluation at Breakthrough, which is a network of organizations founded by Microsoft’s Bill Gates to accelerate clean energy innovation. Toone also serves as a managing partner of Breakthrough Energy Ventures, the venture capital arm of Breakthrough Energy.

“This correlation between energy use and prosperity persists across a huge group of countries, and importantly, there are no high-wealth/low energy-use countries,” said Toone. Worldwide income parallels electricity consumption as they both climb, according to data shared by Toone. And in the past five decades, the middle class has been expanding worldwide, so prosperity has been increasing. 

Given that 80 percent of global energy use is energy from fossil fuels and 35 percent of electricity comes from coal, that prosperity results in emissions, declared Toone. Moreover, a rise in prosperity induces a set of behaviors that contribute to the production of emissions. We build more, drive more, eat more, and so on. Still, we need to decarbonize, and we know how to do it, Toone said.

“Of course we can decarbonize. It’s not hard to do. The question is: How much will it cost?” One approach is to think about how much it would cost to replace each of our business-as-usual products with equivalent zero-carbon products. Considering all products worldwide, and how much extra we’d pay per ton of carbon eliminated, we can calculate the total cost to the global economy to decarbonize.

But, he said, what it would cost is only half the question. The other half is, what are people willing to pay? While wealthy people in the developed world will say some percent, observations of their behavior — “when nobody’s looking” — show that they won’t pay anything at all. Toone added, “And that’s in the OECD world, where we can actually afford to do it!” OECD is the Organization for Economic Cooperation and Development, a world trade organization.

Breakthrough Energy and its collaborators enlisted artificial intelligence to analyze what it would cost to decarbonize the global economy, and AI estimated it would cost about $7.5 trillion. While that’s a big number, global GDP is about $130 trillion, so it’s about 5 percent of global GDP. But Toone also noted that people — even wealthy people — aren’t willing to pay. So the challenge is figuring out how to do it with zero “green premium,” that is, with no extra charge to use a zero-carbon product.

Breakthrough Energy was created in 2015 to do exactly that. Breakthrough started with a $1 billion venture fund, then raised about $5 billion more. Breakthrough has deployed about $3 billion of that into about 140 companies. Toone described the technologies of some of the companies Breakthrough Energy Ventures has invested in to demonstrate the difference they are making.

• Luxwall has developed and is now manufacturing novel windows that are as insulating as walls. The windows are sized to fit into conventional frames. One commercial building recorded a 30 percent energy savings as a result of installing the new windows. Toone predicted that this product would be the “new LED bulb. We all swapped out incandescent for LED bulbs because the energy saving was so big and so obvious. This is what's next.”
• Brimstone is aiming to reduce the 8 percent of emissions that come from making cement. And as the emerging economies come along, they do a lot of building. So, finding a way to decarbonize cement is critical. The company is mining a different source of critical raw materials that contains a lower concentration of the conventional cement feedstock used today, but also yields valuable metals, including aluminum.
• Ecocem is investigating a recent discovery as it also seeks to reduce emissions related to making cement. Controlling the particle size of the cement enables much more effective packing, thus reducing the total amount of cement needed.
• CarbonCure Technologies is adding carbon dioxide to cement as it’s being poured to yield cement that’s harder, resulting in a 10 percent reduction in the total amount of cement that’s needed.

Another challenge in the energy transition is how we make and distribute electricity, and how much that costs. Solar and wind are some of the cheapest sources today, but both are intermittent. Breakthrough companies are addressing two ways to solve that problem: First, by moving the electricity from where it’s abundant to places where it's needed; and second, by storing it when it’s abundant so that it's available when it’s needed. Here are some examples:

• TS Conductor is building transmission wires with a solid core that keeps them from sagging, touching vegetation, and potentially starting fires. “That means I can put up to three times as much energy down an existing transmission corridor,” said Toone.
• VEIR, a company founded by a MITEI Energy Scholar, is using superconducting cables that can transmit 10 times as much power down an existing corridor.
• Form Energy, a spinout from MIT, is developing iron-air batteries that can store power for as long as a month or even a couple of months.

The other thing we could do would be to put our solar collectors where there isn’t an intermittency — for example, space. “If I go to space, the sun always shines,” said Toone. The idea of space-based solar has been around for a long time. The challenge is the cost, particularly the cost of “getting stuff into space,” he said. A Breakthrough company called Stoke Space Technologies is building reusable rockets to solve that problem.

Geothermal energy — that is, the energy that's contained as heat in the Earth — is another option for generating electricity. Fervo Energy, another Breakthrough portfolio company, is drawing on advances in drilling technology that came about for fracking. The company is now building the world's first large-scale geothermal power plant in southwest Utah, the Cape Station power plant. The plant is already generating 500 megawatts — continuously, regardless of the weather at the Earth’s surface.

Another source of always-available power is fusion energy, but the main challenge with fusion is containing materials at temperatures equivalent to temperatures in the core of the sun. A physical vessel is not an option. Containing them in a magnetic field is the approach that’s the furthest along. Commonwealth Fusion Systems, an MIT startup and Breakthrough-supported company, is a leader in this approach. There, researchers have created high-temperature superconducting tapes that generate the enormous magnetic fields needed to contain those high-temperature materials.

The potential of all of these technologies, if successful, could reduce the cost to decarbonize the world economy to $3.5 trillion — half as much as the initial estimate, Toone said.

The $5 billion that Breakthrough is investing is only a fraction of the total global investment in clean energy, Toone said. About $220 billion has been invested in clean technology during that same period of time, and venture capitalists have invested about $2.5 trillion, he said. 

The biggest challenge that Toone sees is that the world has an enormous existing infrastructure that has to be turned over. So, it's critical that those new technologies be deployed in the developing and emerging world so there’s not another enormous infrastructure that has to be replaced.

“We can do this! We can absolutely develop new technologies that provide the exact same service, but at zero carbon,” Toone said. “We can absolutely do this.”

Three technologies developed by MIT Lincoln Laboratory are among recipients of 2026 R&D 100 Awards. The awards recognize the 100 most significant innovations that have transitioned to use or been made available for license in the past year.

R&D World, an online publication, manages the awards program, which has run annually since 1963. The worldwide competition is judged by an independent panel of science and technology experts and industry professionals.

The laboratory's winners overcome obstacles to improving the security and capability of systems. One technology is an operating system designed to protect embedded computers from cyberattacks. Another technology is a platform for integrating superconductor and semiconductor chips in a wafer-scale system for quantum or high-performance computing. The final winner is an algorithm that removes interference 5G base stations incur on co-channel radio frequency (RF) devices. 

"These awarded technologies address critical problems in cybersecurity, quantum computing, and communications. We're proud of our dedicated staff who developed and transitioned these technologies for the nation's advantage," says Melissa Choi, Lincoln Laboratory director. 

Since 2010, Lincoln Laboratory has received 111 R&D 100 Awards recognizing the transfer of its unclassified technologies to industry and government. Each year, many technology transitions also occur for classified projects. This transfer of technology is central to the laboratory's role as a Department of War (DoW) federally funded research and development center.

Secure-by-design operating system

Computers embedded in deployed systems — such as satellites, drones, or radars — keep missions intact and people safe. The operating systems (OS) inside these embedded computers, however, are prime targets for cyberattacks. They are large, complex, and built on decades of legacy designs predating modern cyber threats. Increasingly, artificial intelligence is helping adversaries exploit OS vulnerabilities. 

To protect embedded systems, Lincoln Laboratory developed an inherently more secure OS called Magnetite. "If Windows and Linux are the familiar workhorses of general-purpose computing, Magnetite is built for the different realities of embedded systems, for which real-time performance matters, resources are tight, and system failure has physical consequences," says Juliana Furgala, principal investigator in the Secure Resilient Systems and Technology Group. 

A few components assure Magnetite's security. It is built on top of seL4, a formally verified microkernel (i.e., software core). Formal verification means that mathematical proofs have guaranteed the microkernel's code behaves exactly as intended, eliminating a source of security flaws. On top of this foundation, Magnetite’s OS services — such networking, storage, and communication — are written in Rust. This programming language prevents common memory-corruption bugs that hackers often exploit. Finally, Magnetite configures its services as isolated processes under the principle of "least privilege." This configuration prevents different parts of an embedded system from accessing unnecessary data. If a cyberattack compromises one service, that component can be locked down while critical functions of the system continue running.

Free for government use and approved partners, Magnetite is now being integrated into space and flight platforms for the DoW. Looking ahead, the team sees potential to extend the platform to other embedded domains, such as control systems for critical infrastructure. Because Magnetite offers a common, reusable foundation for building specialized mission capabilities, the team is also collaborating with other technical groups across the laboratory to offer Magnetite as an alternative to costly, proprietary operating systems currently in use.

In addition to earning an R&D 100 Award, Magnetite was selected as MIT Lincoln Laboratory's Best Invention of 2025.

Wafer-scale platform for quantum systems

Modern computing systems are increasingly limited not by transistor performance, but by interconnection bottlenecks. Electrical pathways linking densely packed chips are taking up space, slowing data transfer, and generating heat. The problem is especially pronounced in quantum systems, which require bulky coaxial cables to bridge superconducting chips (which must be cryogenically cooled to achieve zero electrical resistance) with room-temperature control electronics.

The Quantum Microsystems platform addresses this interconnection challenge, integrating hundreds of superconductor and semiconductor chips into a single, wafer-scale system. The platform combines several innovations. First, an AI decision-tree approach determines the optimal way to route connections between chips across a wafer. After the wafer is tested for defects, a digital lithography process "draws" custom wiring layers between chip locations and around defective circuit components to enable the highest yield possible from the wafer. Chips are then connected using microbump (tiny "bumps" of conductive material used to connect stacked chips) or bumpless techniques that place them extremely close together; a superconducting fabric extends these connections across the wafer to create a unified computing platform, carrying signals at near light speed. Coaxial cables are replaced with integrated, lithographically defined flexible interconnects, significantly reducing cryogenic wiring overhead and system complexity. 

The Quantum Microsystems platform is designed to be reworkable, so chiplets can be replaced or upgraded without rebuilding the entire system. The platform enables the design and fabrication of systems ranging from individual and stitched‑reticle architectures to mix‑and‑match multireticle configurations and fully integrated wafer‑scale platforms. The platform's AI engine continuously adapts to process variations and design changes to enable efficient, data-driven microsystem manufacturing. 

"By shifting from chip-level scaling to AI-optimized system-level integration, the platform establishes a new paradigm for building ultra-efficient, large-scale computing infrastructure for applications in quantum computing, AI, and high-performance computing," says Rabindra Das, principal investigator in the Quantum-Enabled Computation Group. The technology is designed for transition to government and commercial foundries and may be available for licensing and collaborative development through Lincoln Laboratory’s technology transfer process.

Algorithm to cancel 5G interference 

As 5G cellular networks have expanded, so has an unintended source of interference. In certain environments, powerful signals emitting from 5G base stations can encroach into frequency bands used by nearby non-5G RF antennas, burying signals for such systems as military radars, aircraft altimeters, satellite ground stations, or radio telescopes. 

To cancel this interference, Lincoln Laboratory researchers developed an algorithm that isolates and removes the 5G signal from the affected antenna's signal. Because 5G networks adhere to a published international standard, the canceler can decode the structure of a nearby base station's transmission — and estimate distortion caused by multipath scattering in the environment — well enough to reconstruct a copy of the interfering signal. That reconstructed copy is then subtracted from the incoming data, leaving a clean signal.

Unlike traditional interference cancellation approaches like beamforming, the technique does not depend on spatial separation of interfering and desired signals. Moreover, because it removes interference on each individual antenna channel of the receiver, it preserves the antenna resources that may be needed to handle other types of interference, such as jamming.

The team validated the algorithm using signals captured over the air from a Nokia 5G base station at Hill Air Force Base. The tests recovered a test radar signal buried by the cellular interference. The signal was restored to within two decibels of its original signal-to-noise ratio, translating to 89 percent preservation of the radar's interference-free detection range. 

"While 5G waveforms exhibit complex structure, their generation is governed by global standards that allow our cancellation methodology to generalize across base station radio heads made by a diverse set of vendors," says Binoy Kurien, principal investigator in the Tactical Edge Communications Group. 

The DoW is now evaluating the technology for use on multiple platforms. Because the core structure of the 5G waveform is expected to carry into future cellular generations, this approach may also extend to interference from 6G networks as they emerge.

Accurate, up-to-date climate projections are essential for planning decisions that enhance the resilience of power grids, protect public health, maximize agricultural yields, and more.

Now, MIT researchers have developed a technique that streamlines the production of fast, trustworthy, and adaptable artificial intelligence models for climate prediction.

These specialized AI models, known as climate emulators, can rapidly simulate the effects of human activities onto a future climate, predicting impacts on temperatures or sea level, for example. 

Because emulators run in a matter of seconds, scientists use them to efficiently test thousands of varied climate scenarios. This provides more reliable estimates and can identify unexpected outcomes of regional interventions.

The MIT researchers developed a training technique that boosts the performance and adaptability of a climate emulator while using only a fraction of the data typically required. This significantly cuts production time for these practical scientific tools.

They show that an emulator trained with a much smaller amount of carefully selected synthetic data accurately predicts outcomes of scenarios it did not see during training, while traditional emulators failed to adapt to new situations.

“Climate emulators are especially well-suited for modeling policy-relevant scenarios. By using our method to generate optimized training data for an emulator, we can speed up the production of much more reliable emulators, so we can better trust their outputs for making policy decisions,” says Christoher Womack PhD ’26, who is now a postdoc at ETH Zurich and lead author of a paper on this technique.

He is joined on the paper by Shahine Bouabid, a postdoc in the MIT Department of Earth, Atmospheric and Planetary Sciences (EAPS); Andrei Sokolov, a research scientist in MIT’s Center for Sustainability Science and Strategy (CS3); Popat Salunke, a postdoc in CS3; Glenn Flierl, a professor of oceanography in EAPS; Sebastian D. Eastham, an associate professor at Imperial College London; and senior author Noelle E. Selin, a professor in the MIT Institute for Data, Systems, and Society (IDSS) and EAPS, and director of CS3. The research appears this week in the Proceedings of the National Academy of Sciences. 

Speedier surrogates

Climate scientists typically use earth system models (ESMs) to make long-term projections about how global temperature or precipitation could change in response to varying greenhouse gas emissions.

These enormous, physics-based models capture the major components and interactions of the earth’s climate in extreme detail. While powerful, ESMs can take months to run on a cluster of supercomputers — and that’s only to project one possible future climate scenario. 

Selin and her collaborators design climate emulators, which are fast AI surrogate models that mimic the behavior of full-scale ESMs. Emulators help scientists test many scenarios, make more accurate forecasts, and explore specific pollutants and regional effects.

“Climate emulators can run in just a few seconds, so it really expands what we can do in terms of exploring these future climate scenarios,” Womack says.

When scientists design a climate emulator, they often train the AI model using a set of six to eight future scenarios based on realistic future trajectories of emissions. They must run each scenario through a full-scale ESM, making these datasets costly and time-consuming to generate. 

The MIT researchers recently found that using realistic future scenarios as training data can lead to underperforming emulators that fail to predict unfamiliar or novel scenarios. 

In this work, they developed an algorithm that generates a synthetic dataset of optimized climate scenarios tailored to boost one emulator’s predictive performance.

“Our algorithm might generate some weird scenarios which look nothing like a realistic future, but we don’t care so much if the training scenarios are realistic. We are just trying to make the best possible dataset to train an emulator,” Womack says.

Importantly, rather than needing to run a full ESM six or eight times to generate training data, a user would only need to run that model once.

Tuning the training data 

The researchers begin by generating an arbitrary climate scenario using a fast and simple climate model. They test how well their emulator can reproduce the results of that ESM, which gives them a measure of error.

Because of the programming technique they used to build the training pipeline, they can iteratively update the training data to increase the emulator’s predictive skill. For instance, the algorithm could increase CO2 emissions dramatically in the year 2050 or rapidly decrease methane emissions in 2037.

Then the optimization updates the training data until the emulator’s error level reaches a certain threshold.

In the end, rather than resembling realistic scenarios in which emissions follow smooth trajectories, the synthetic data contain rapid variations.

“By introducing these high-frequency wiggles and oscillations, our data enable the emulator to extract a lot more information about the overall system,” Womack explains.

Their training algorithm outputs these emissions scenarios, which they can use to train an emulator that is more accurate and adaptable.

“In the future, we are definitely going to see climates and scenarios we haven’t seen before. If we can simulate these different scenarios more robustly, we can provide more reliable information to the stakeholders who need it,” Selin says.

For instance, an emulator trained with their technique can capture the effects of local pollutant control policies that reduce one pollutant while other emissions continue rising. The traditional emulators they evaluated failed this test.  

In the long run, Womack hopes this work improves the usefulness of fast and adaptable climate emulators for policy formulation. 

“This is such an important piece of the pipeline when it comes to creating useful tools that speak to stakeholder needs. Because the process of training an emulator can takes months or even years, the emulators we have are usually out-of-date. By cutting down that production time, I think this technique will be a game-changer in terms of getting updated tools into the hands of researchers and practitioners,” Selin adds.

In the future, the researchers want to test their technique using an emissions scenario generated by a full ESM. They also want to apply it to other types of climate models and explore its effectiveness in broader machine learning and computer vision applications.

This research was part of the Bringing Computation to the Climate Challenge (BC3) project and supported by Schmidt Sciences through the MIT Grand Challenges.

Order has long been the rule in the design of synthetic “metamaterials,” which have internal structures that give the whole material exceptional properties not found in nature.

When scientists engineer a metamaterial, they design from a single building block, or “unit cell,” that is then copied and pasted many times over. The result is a precise, periodic microstructure that results in improved material performance. 

Now, a team from MIT and Northeastern University has found that engineering a little disorder into a material can further enhance its “meta” features. 

In an open-access study appearing today in the journal Nature, the researchers introduce a new way to design metamaterials, inspired by the types of networks that appear in a leaf’s vasculature, the veins in a dragonfly’s wings, and the virtual connections in a social network. These new metamaterials — termed “scale-rich metamaterials” — exhibit structures of varying sizes, which sprawl and connect to form a physical network.

For decades, networks have been used as theoretical constructs to describe everything from cities to the internet to connections between neurons. The team has now applied such network designs to physical materials. They designed metamaterials made not from repeating unit cells, but from tiny support beams of varying sizes, similar to a network’s dense, web-like patterns. 

When subjected to physical tests, the new materials attained a widely controllable range of stiffness and held up against sudden impacts better than conventional, ordered metamaterials. They also were able to cushion and protect fragile objects embedded within the materials.

The team designed their materials using an algorithm which generates an optimal network of microstructures for a desired material property. The procedure can be used to design resilient, impact-absorbing materials such as helmets and armor, as well as acoustic lenses — materials whose microstructure efficiently focuses sound or vibrations.

“This is a completely new way of thinking for metamaterials,” says study author Carlos Portela, associate professor of mechanical engineering at MIT. “Unlocking this network design for materials really frees us to think of new microstructures and mechanical benefits that haven’t been possible before.” 

The study’s MIT co-authors include Andrew Yen-Jong Chen, Mohammad Charara, and Jayna Wadhwa, along with Csaba Both, Ting-Ting Gao, and Albert-László Barabási of Northeastern University, and Niek Mooij of Utrecht University in the Netherlands.

Looking beyond order

Network science is a field of research that studies complex networks, the nodes and connections within a network, and how these elements relate to each other and to the network as a whole. Network science has been used to map and characterize a wide range of systems, from networks in nature, such as food webs, evolutionary trees, and neural connections, to financial markets, cellular and social networks, and the internet. 

Albert-László Barabási, a co-author of the new study, has been at the forefront of network science. A professor of physics at Northeastern University, Barabási explores the structure of complex networks across many disciplines, studying where a network may exhibit vulnerabilities and how it could be made more robust.

Most of Barabási’s work has involved conceptual networks, or networks that are mapped onto existing systems. Recently, he wondered: Could network science be applied to build a physical material with certain desired properties that are not possible in nature? 

For this, Barabási joined efforts with Portela, whose group at MIT specializes in metamaterials. The researchers engineer materials from the ground up, with precisely patterned microstructures that enhance the material’s performance overall. Like others in the field, Portela has built metamaterials from a single unit cell, repeating this building block in patterns such as square lattice structures and honeycomb architectures.

In many cases, Portela has found that such ordered patterns do improve a material’s strength and resilience. But if these materials are punctured or cracked, the rest quickly buckles and fails. He says that’s because the internal structure is all the same; if one region of the material is deformed, the surrounding, similar unit cells will respond and deform in kind.

“We’ve spent years looking at periodic and ordered microstructures, and we’ve learned there are a lot of limitations and non-ideal responses, such as this localization effect,” Portela says. “We wanted to break free of that notion and use network science to design these materials.”

Line by line

The team developed an algorithm to virtually generate a network of beams with the constraints of a physical material. When applying network science to something like the internet, researchers haven’t had to consider physical constraints, such as limiting the number, density, or intersection of connections. In a physical material, the number and thickness of beams, and the space between them, start to matter. 

To design a physical network, the team’s algorithm starts by drawing a point at a random coordinate in a domain representing the size of the final material. The program draws a line at a random angle, which stretches through the point and crosses the entire space. The program then repeats the process with a second point, at a second random coordinate, and a line drawn through the point at another random angle, until that second line reaches a boundary, of either the whole domain, or the first drawn line. The process then repeats, again and again, drawing progressively shorter, thinner lines. The resulting material represents a dense network of intersecting beams of many varying sizes. 

Scientists can vary the type of network that the program generates, by varying the thickness of the starting line and how fast that thickness decreases with each successive line drawn. By simply tuning these two parameters, the team found they could generate a huge range of networks. They then carried out simulations to predict how tuning one or the other, or both, by varying degrees, would change a material’s stiffness and other physical properties. 

“We see we can really tune the stiffness of a material just by changing these two parameters, which dictate how these network patterns get generated,” Portela says. 

The team then fabricated physical materials with microstructures that were patterned after the networks they virtually generated. They used 3D-printing methods to deposit polymer into patterns of tiny, interconnecting beams. Each resulting sample was a large square, about the size of a credit card, with internal beams that ranged from microns to centimeters in scale. 

They put the samples through various physical tests, such as dropping a large weight onto them and squeezing them with increasing force. They recorded the materials’ response, along with samples of conventional lattice and honeycomb metamaterials. The network-inspired samples, they found, were more resilient to compression and impacts. 

“Because there is such a richness of length scales in these networked materials, if one part fails, there is a strong web of structures around it that keeps the entire material from failing catastrophically,” Portela says. “It’s more efficient at distributing damage and absorbing energy.”

As another demonstration, the researchers designed a material inspired by the density-graded microstructure of a pomelo’s thick rind. They designed the material with edges made from a denser network of beams, and showed it could cushion and protect a fragile object that was embedded at the center of the network.

“This work provides a design framework for materials whose features vary over a very wide range,” says materials expert Tian “Tim” Chen, associate professor in the Department of Aeronautics at Imperial College London, who was not involved with the study. “That may lead to a next generation of materials with unprecedented physical behavior, for any structure that must carry load, absorb energy or control vibration.”

This work was supported, in part, by the U.S. National Science Foundation’s Designing Materials to Revolutionize and Engineer our Future (DMREF) program.

The MIT Schwarzman College of Computing welcomes seven new faculty members in core computing and shared positions to the MIT community in 2026. They’re inventing and using artificial intelligence and computing technologies in new ways to transform areas such as environmental science, quantum computing, human-AI interaction, and macroeconomics and finance.

“It’s a pleasure to welcome these outstanding faculty members to MIT, whose innovative research is leading advances in AI and computing and its impact on society,” says Dan Huttenlocher, dean of the MIT Schwarzman College of Computing and the Panasonic Professor of Electrical Engineering and Computer Science.

These hires include core faculty with appointments in the Department of Electrical Engineering and Computer Science (EECS), which reports jointly to the MIT Schwarzman College of Computing and School of Engineering, as well as shared faculty positions between the college and other departments, including Urban Studies and Planning, Mathematics, Economics; and, at MIT Sloan School of Management, the Finance Group and the Work and Organization Studies Group.

The new shared faculty include:

David Bruns-Smith, an assistant professor at the MIT Sloan School of Management, who holds an MIT Schwarzman College of Computing shared position with EECS, joined MIT in September. He is also a member of the Laboratory for Information and Decision Systems. His recent research has focused on debiased machine learning with applications in macroeconomics and household finance. Previously, he was a postdoc at Stanford Data Science, and he completed his PhD in computer science at the University of California at Berkeley.

Zana Buçinca, an assistant professor at the MIT Sloan School of Management, who holds an MIT Schwarzman College of Computing shared position with EECS, joined MIT in September. She is also a member of the Laboratory for Information and Decision Systems. Her research interests lie at the intersection of human-AI interaction and the future of work, and involve designing human-AI interaction techniques that complement people and amplify their values in AI-assisted work. She was a postdoc at Microsoft in the Office of Applied Research, and received her PhD in computer science from Harvard University.

Alexandra Cabral, an assistant professor in the Department of Urban Studies and Planning, who holds an MIT Schwarzman College of Computing shared position with the Institute for Data, Systems, and Society (IDSS), joined MIT in September. Her research focus includes low-cost sensor networks, smart cities, environmental informatics, and low-resource computing. Previously, she was a postdoc in the Ka Moamoa Lab at Georgia Tech and served as a 2025-26 NSF ICICLE Educational Fellow. She earned her PhD in computer science from Harvard University.

Alexandru Damian, an assistant professor in the Department of Mathematics, who holds an MIT Schwarzman College of Computing shared position with EECS, joined MIT in September. He is also a member of the Laboratory for Information and Decision Systems. His work is focused on developing a predictive, and ultimately prescriptive, theory of deep learning. He holds a BS in mathematics from Duke University and a PhD in applied and computational mathematics from Princeton University.

Lindsey Raymond PhD ’24, an assistant professor in the Department of Economics, who holds an MIT Schwarzman College of Computing shared position with EECS, joined MIT in July. She is also a member of LIDS. Her research involves the effects of digital technologies and artificial intelligence on labor markets, firms, and market competition. She was a postdoc in economics and computation at Microsoft, and earned her PhD from MIT Sloan.

The new core faculty are:

Jas Brooks, the KDD Career Development Professor in Communications and Technology, joined EECS as an assistant professor in September. They are also a member of the Computer Science and Artificial Intelligence Laboratory. Their work focuses on perceptual engineering: the design of computational interfaces that sense and modulate human sensory perception. Previously, they were a Rising Star in EECS, a Siebel Scholar, and a National Science Foundation Graduate Research Fellow. They hold a BS and PhD in computer science from the University of Chicago. 

Hengyun (Harry) Zhou ’16 joined EECS as an assistant professor in March. He is also a member of the Research Laboratory of Electronics. His research focuses on accelerating the development of large-scale, fault-tolerant quantum computers through full-stack architectural innovation. Before coming to MIT, he previously served as the Quantum Error Correction Architecture Lead at QuEra Computing. He earned his PhD in physics from Harvard University.

The MIT School of Engineering welcomes new faculty members across six of its academic units in 2026. This new cohort of faculty members, who have recently started their roles at MIT, conduct research across a diverse range of disciplines.

“I’m pleased to welcome these talented colleagues to our engineering faculty,” says Paula T. Hammond, dean of engineering and an Institute Professor. “They bring fresh perspectives that will open new directions for research and create opportunities for our students to learn through discovery. I look forward to supporting them as they pursue ambitious ideas, build collaborations across MIT, and help our students grow, both in the classroom and in the lab.”

Faculty with appointments in the Department of Electrical Engineering and Computer Science (EECS) and the Institute for Data, Systems, and Society (IDSS) report into both the School of Engineering and the MIT Stephen A. Schwarzman College of Computing.

The new engineering faculty include:

Andrew Akerson joined the Department of Mechanical Engineering as an assistant professor last January. His research interests include optimal design, computational solid mechanics, and material characterization. He previously worked as a research scientist at Meta's Reality Labs Research. Akerson received his BS and MS in aerospace engineering and mechanics from the University of Minnesota and his PhD in mechanical engineering from Caltech.

Jake Hecla ’17, the Charles Stark Draper Career Development Professor, joined the departments of Aeronautics and Astronautics and Nuclear Science and Engineering as an assistant professor in July 2026. His current research interests focus on aerospace nuclear power and propulsion, heat rejection in space, and long-standoff characterization techniques for space nuclear systems. Previously, Hecla was a Stanton Postdoctoral Fellow in the MIT Laboratory for Nuclear Security and Policy. He holds a BS in nuclear science and engineering from MIT and an MS and PhD in nuclear engineering from the University of California at Berkeley.

Zachary Manchester, the Boeing Career Development Professor, joined the Department of Aeronautics and Astronautics as an associate professor in January. He is interested in building autonomous systems that match, and eventually surpass, the levels of performance, agility, and reliability of humans. He previously taught in the Robotics Institute at Carnegie Mellon University. Manchester received his BS in applied physics, his MEng in mechanical engineering, and his PhD in aerospace engineering from Cornell University.

Eric Sun joined the Department of Biological Engineering as an assistant professor in January. He develops new computational and machine learning models to study complex biology across multiple scales with applications in aging, rejuvenation, and immune interactions. Sun is also a member of the Ragon Institute of Mass General Brigham, MIT, and Harvard. He obtained his BS in chemistry and physics and his MS in applied mathematics from Harvard University and his PhD in biomedical informatics from Stanford University.

Haoning Tang joined the Department of Mechanical Engineering as an assistant professor in January. She conducts interdisciplinary research at the intersection of nanophotonics, optical instrumentation, and device engineering, with a focus on metamaterials, quantum materials, and MEMS-enabled platforms. She was previously an adjunct professor in the electrical engineering and computer science department at the University of California at Berkeley. Tang received her BS in electrical and electronics engineering from Hong Kong University of Science and Technology and her PhD in applied physics from Harvard University.

Stylianos Varchanis, the Raymond A. (1921) and Helen E. St. Laurent Career Development Professor, joined the Department of Chemical Engineering as an assistant professor in July. He develops theoretical and computational models to understand fundamental transport processes in complex fluids, soft matter, and living systems, with relevance to materials engineering, energy, and biotechnology. Before joining MIT, Varchanis was a Flatiron Research Fellow at the Flatiron Institute of the Simons Foundation in New York. He received his BS, MS, and PhD in chemical engineering from the University of Patras in Greece.

Hengyun Zhou ’17, the Thomas D. and Virginia W. Cabot Professor, joined the Department of Electrical Engineering and Computer Science as an assistant professor in March. He is developing full-stack architectures for resource-efficient, fault-tolerant quantum computing. His work spans quantum error correction, algorithms and compilation, and hardware architecture. Zhou previously worked as a research scientist for QuEra Computing Inc. He holds a BS in physics and mathematics from MIT and a PhD in physics from Harvard.

The School of Engineering is also welcoming Edward “Jas” Bordeau Brooks, the KDD Career Development Professor in Communications and Technology, who joined the Department of EECS as an assistant professor in September 2026. For more information about Brooks, read the Schwarzman College of Computing’s recent article. 

The School of Engineering also remembers Thomas Rose, MIT’s POSCO Professor of Materials Science and a member of MIT’s Center for Materials Research in Archaeology, who joined the Department of Materials Science and Engineering’s faculty in January. Rose died on Sept. 2 following a bicycle accident. His research focused on ancient metallurgy and pyrotechnology that shaped early human societies, including how copper and its alloys were produced, transformed, and circulated. The work integrated geochemistry, mineralogy, experimental archaeology, and materials science.

A senior studying mechanical engineering and finance, Dominic Hess is an Academic All-American with a packed study schedule. But he and his teammates still make room for about 20 hours a week of football during the regular season. (“It’s a little less in the spring,” he notes.) All this time practicing, working out, and playing in games, he says, doesn’t take away from his academic experience at MIT — it amplifies and improves it. In this interview, Hess discusses his experiences on and off the field at the Institute, as well as plans for the future.

Q: How would you compare your high school football experience to your MIT one? 

A: I'm originally from a small town, Damascus, in Maryland. On my way to high school, I would drive past seven farms and a lot of cornfields. 

Football was big there, and the town was historically really good; I think we’ve won 12 state championships. I grew up playing several sports and decided to stick with football going into high school. Being in a super-competitive environment was a great experience. In my freshman year, the No. 1 player in the nation was on my team. And as a 5'10", 135-pound kid playing with a guy who was 6'6" and 280 pounds, it was crazy — a very high level of football. 

I took my academics very seriously and decided I wanted to keep playing in college, but I knew I wasn’t good enough to play at a Division I school. Luckily, I ended up here — and it has been the most impactful four years of my life.

I’ll say, coming to MIT, it was different. I tell people that I play football here, and they're like — “MIT has a football team?” 

Q: How do you strike a balance between academics and football? 

A: It definitely took some getting used to, especially during freshman year. 

Football actually helped a lot. We come to campus in August, a few weeks before classes start. As a freshman, I had no idea about what classes to take, what the numbers all mean, what the names of things are — all that stuff. But I had all this time where I could talk to my teammates about careers, different majors, and what classes to take. I learned a lot. 

Once school starts, you're taking classes that are harder than anything you've ever taken in high school, and the workload is way more than you’ve had before. It's a huge adjustment, and the balance is not easy — but you get to do it with other people. The community in an athletics team, having built-in friends who can support you, really benefits you on the academic side. 

Also, to me, it’s a grounding thing. I have all this other stuff going on — academics, clubs, my fraternity, whatever. When I walk onto the practice field, I say: “I'm just going to focus on football right now.” Those two hours are the one time of my day when I don't have to worry about anything else. It's a blessing to be there.

Q: What was it like when you became a captain of the team? 

A: I was captain of my high school team, but coming into MIT was a reset. You're back to being a freshman, back to the bottom of the lineup, just trying to earn your stripes. You need to do the work to become a good player and a good teammate. Over my first three years, I was able to improve a lot. I guess my teammates and my coaches recognized that, and I ended up getting selected. 

I think a key thing about being a leader, especially on a sports team, is asking your teammates, What are your goals? And then, just as important, What steps are you going to take to get there? I really want to win a NEWMAC [New England Women's and Men's Athletic Conference] championship, and, obviously, the guys on the team want the same thing. Taking the steps to get there is what's important. You're not going to win a championship in a random way in December, but you can do something today and keep stacking it and getting better and better. 

Being a captain on the field is a little different. It's really about keeping players motivated. I've been watching this documentary about Nick Saban, the Alabama football coach. He talks about each play being six seconds — and all you have to do is focus on this play and go all-out for six seconds. 

Q: What motivates you, on and off the field?

A: I'd say, especially at MIT, the community really motivates me. I grew up with three brothers and was very close to my parents. My dad’s in tech consulting and worked at home, so I got to spend a lot of time with him growing up. My mom quit her job to raise us. Four boys is a lot of work. (They've made it to every game this season; my mom always jokes how this season will be the end of her “sports-mom” era.)

And so, coming here, I guess I was looking for a new family — and I was fortunate to find it in football. You hear a lot of things about MIT, like how hard it is and how students can feel alone. It can obviously be hard here, so I wanted to find that community. 

I've been with the same roommate all four years; we’ve gotten very close. I’m also in a fraternity, DKE [Delta Kappa Epsilon]. Also, being a part of the Catholic community has been really important. It turns out a lot of the guys on the team are also Catholic. I didn't know I'd find that here.

Q: What’s after MIT for you?

A: I interned in New York City the past three summers and am fortunate to have a job lined up for next year. My two older brothers already live and work there, so I'm planning to move in with them. Both my parents are actually from New York, so unfortunately that means I'm also a Jets fan, and unfortunately, now, I can say it means being a Yankees fan. But the Knicks…. 

Eminent theoretical physicist Jeffrey Goldstone, an MIT professor emeritus, Dirac Medalist, and holder of the Department of Physics’ Cecil and Ida Green chair, died Oct. 12 at age 93. 

A member of the MIT physics community for 49 years, Goldstone was especially known for his influential work in quantum field theory as well as contributions to quantum optimization algorithms. Notably, he is the namesake of the Goldstone boson and the related Goldstone theorem.

“Jeffrey was a giant of theoretical physics who will be long remembered and very much missed,” says Tracy Slatyer, director of the MIT Center for Theoretical Physics – a Leinweber Institute (CTP-LI) and professor of physics. “He contributed greatly to the CTP-LI over the years, and it was an honor to have him as a colleague.”

“Goldstone was a brilliant theoretical physicist and a charming, yet diffident and remarkably humble human being,” says Frank Wilczek, a CTP-LI colleague who is the Herman Feshbach Professor of Physics, Emeritus, and a 2004 Nobel laureate. “He made renowned, fundamental contributions to diverse areas of physics, ranging from condensed matter and quantum computing to string theory. Throughout, you can perceive a consistent style: enormous mathematical talent brought to bear on important, concrete, sharply posed physical problems.”

Goldstone is renowned for his remarkable range of groundbreaking discoveries. He began his career as a nuclear theorist. In his early work on nuclear matter with Hans Bethe, he developed the use of Feynman diagrams for non-relativistic many-fermion systems.  

He later turned to particle physics, investigating relativistic field theories with spontaneously broken symmetries. Building on ideas inspired by superconductivity, he demonstrated that spontaneously broken continuous symmetries must be accompanied by massless, spinless particles — now widely known as Goldstone bosons (or Nambu-Goldstone bosons, in recognition of Yoichiro Nambu’s earlier related insights). 

In subsequent work with Abdus Salam and Steven Weinberg, Goldstone formalized and generalized this result, which became known as the Goldstone theorem. With Claudio Rebbi, Charles Thorn, and Peter Goddard, he worked out the light-cone quantization of relativistic strings, a foundational result in string theory. Goldstone also worked with Roman Jackiw and Frank Wilczek on solitons in quantum field theory.  

Goldstone was born in 1933 in Manchester, England. He earned his PhD from Trinity College at the University of Cambridge in 1958 and completed postdoctoral work at Cambridge, Harvard University, CERN, and Copenhagen University. After shifting his focus to particle theory, he joined the faculty at Cambridge in 1962. He moved to MIT as a professor of physics in 1977 and as a researcher in the Laboratory for Nuclear Science, and was appointed the Cecil and Ida Green Professor of Physics in 1983. Goldstone served as director of the Center for Theoretical Physics (now known as the CTP-LI) from 1983 to 1989.  

“Goldstone’s name will always be associated with spontaneous symmetry breaking, where 'Goldstone’s theorem' and 'Goldstone bosons' feature prominently,” Wilczek notes. “His early, seminal papers relating the mathematical concept to physical phenomena gave firm foundations upon which people built fruitful theories of magnetism, meson physics, and eventually the Standard Model and early universe cosmology.

“Jeffrey Goldstone’s ideas are so deeply woven into physics that it takes an occasion like his passing to remind us how much we owe him. I had the pleasure to work with Jeffrey in the early days of fractional charge. The short paper we wrote together is one that I’m especially proud of. It contains the seeds of many later developments. Thank you, Jeffrey, for that fond memory.”

Alan Guth ’69, PhD ’72, MIT’s Victor F. Weisskopf Professor of Physics, Post-Tenure, says, “Jeffrey was a wonderful friend and colleague, and a towering figure in theoretical physics. When I was trying to arrange to come back to MIT after being a postdoc for nine years at other places, it was Jeffrey who I contacted to try to make this happen. When I first learned in 1981 that Andrei Linde had figured out how to make cosmic inflation work, in the form known as 'new inflation,’ I remember that I ran into Jeffrey's office to share my excitement. I have learned a great deal from Jeffrey over the years, and will forever have warm memories of our friendship.”

“Jeffrey Goldstone was a theoretical physicist of rare talent and impact,” says David Kaiser, MIT professor of physics and the Germeshausen Professor of the History of Science. “His landmark article, 'Field theories with superconductor solutions,' published in 1961, helped to launch the modern era of symmetry breaking in high-energy physics. Building on insights from neighboring fields such as condensed-matter physics, Goldstone demonstrated that for any model in which a continuous symmetry is spontaneously broken, there will emerge zero-mass, zero-spin particles now known as 'Goldstone bosons.' His watershed study, and several influential follow-up articles, have become central to our Standard Model of particle physics, with further application as far afield as early-universe cosmology to spin waves in magnetic materials and quantized vibrations in excited fluids. Later in his career, Goldstone turned his attention to quantum information science, helping to construct several novel algorithms for quantum computation. 

“Typically reserved, his close friends admired his droll humor. Writing to Harvard physicist Sidney Coleman in November 1976, for example, Goldstone declined Coleman’s invitation to attend an upcoming physics workshop. 'I expect to be in total confusion during January [1977] and wish to simplify my life,’ Goldstone replied. 'Forty-three years in the womb’ — including decades at his alma mater, the University of Cambridge — 'have unfitted me for coping with normal trivial details of survival. It’s probably a good thing I’m coming out, but the birth pangs are hell.’ 

“The birthing event to which Goldstone referred was his move to MIT to join the Physics Department faculty in 1977. Thank goodness he braved the move. Over nearly 50 years, Jeffrey Goldstone enriched our Department of Physics at MIT, and especially our Center for Theoretical Physics – A Leinweber Institute, with his deep insights and gentle manner.”

Even after retiring from MIT, he remained active in research for the next two decades, working in quantum computation. 

Edward Farhi, professor emeritus of physics and a frequent collaborator for over 20 years, describes Goldstone’s contribution to the field from its early stages as “very significant.”

“FFG [Farhi, Goldstone, and Samuel Gutmann] teamed up with Donner Professor of Mathematics Michael Sipser and showed that there was a problem which could not be sped up with a quantum computer, learning parity in the oracle setting,” Farhi says. “This was the first demonstration of this kind of limitation, although the same week another group proved the same thing by a different method. In 2000 FGG, along with Sipser, introduced the concept of Quantum Computation by Adiabatic Evolution. This is an all-purpose approach to optimization and was later shown to be universal, which means that any quantum computation can be encoded as quantum adiabatic. Furthermore, the company D-wave was founded to run this algorithm. D-wave is the progenitor of all of the superconducting hardware efforts such at the ones at Google and IBM. We later showed that using scattering theory a quantum computer could determine who wins a game provably faster than the best classical algorithm for this task.

“FGG introduced the Quantum Approximate Optimization Algorithm. This was the first time a general purpose quantum optimizer could provide worst-case performance guarantees for combinatorial optimization problems. These guarantees as of now (with one exception) do not beat the best classical. Still, this paper has over 6,000 citations, making QAOA one the most-studied quantum algorithms.

“In 2019, FGG and Leo Zhou applied the QAOA to the Sherrington-Kirkpatrick model. We showed how to analyze performance in the infinite size limit at any depth. Our work and others indicated that as depth increases with optimal parameters, the QAOA would approach the celebrated Parisi value. Recently the Open AI math dump, the conjecture that the QAOA would hit Parisi, was proven by the AI. I bet Jeffrey would have been surprised and impressed!”

Jeffrey Goldstone was a fellow of the American Physical Society Fellow, the Royal Society, and the American Academy of Arts and Sciences, and he was an honorary fellow of Trinity College, Cambridge. 

His honors included the Guthrie Medal of the Institute of Physics, the Dannie Heineman Prize for Mathematical Physics of the APS, and with Stanley Mandelstam the Dirac Medal (1991) of the Abdus Salam International Centre for Theoretical Physics, awarded for “his fundamental clarification of the phenomenon of spontaneous symmetry violation in relativistic field theory.”

He is survived by his son Andrew Goldstone, associate professor of English at Rutgers University’s New Brunswick campus; his daughter-in-law Anne Dewitt, a clinical associate professor at New York University’s Gallatin School of Individualized Study; and his granddaughter, Helen. His family is planning a memorial in 2027. 

MIT researchers will lead an up to $53 million effort, awarded by the U.S. Advanced Research Projects Agency for Health (ARPA-H) as part of its STREAM (Systems for Tracking and Resilience in Efficient Agricultural-input Management) program, to develop next-generation weed killers that help farmers and protect human health. 

More than 1 billion pounds of herbicide are used in the United States every year. Herbicides help farmers by preventing significant crop loss due to weeds, and they enable regenerative farming practices that protect the soil, but some weeds are becoming resistant to the most common herbicides. 

Exposure to herbicides may contribute to increased risk of cancer, metabolic and endocrine disorders, and other adverse health outcomes. Farm workers can be exposed from frequent herbicide use, but many U.S. residents are also exposed to these chemicals in small amounts through food and drinking water. 

The MIT-led research team will develop new generative artificial intelligence tools to help them discover herbicide alternatives that are more effective and developed with human health in mind. Such alternatives could also help to reduce U.S. dependency on imported phosphorus, which is a critical ingredient in a major herbicide, glyphosate.

“With herbicides like glyphosate and atrazine, there’s a real crisis because of resistance mutations that are coming,” says Christopher Voigt, head of MIT’s Department of Biological Engineering. “And if you combine the resistance mutations with issues around national security of the supply chain of the precursors to herbicides, we could find ourselves without these core agrochemicals that are absolutely essential for getting the high crop yields and economical prices that the United States now sees.”

Voigt is the principal investigator for the project, known as Precision Learning for On-target Weed control (PLOW). The team is also expected to include researchers from the Broad Institute of MIT and Harvard, the University of North Carolina at Chapel Hill, the University of Tennessee at Knoxville, the University of Illinois at Urbana-Champaign, and Robigo.

The researchers are grouped into three teams that will tackle different aspects of the project, aligned with the STREAM Technical Areas. The generative AI group will develop models that can predict molecules — either chemical compounds or small interfering RNA (siRNA) — that disrupt the same plant enzymes targeted by glyphosate and other herbicides.

Glyphosate kills weeds by interfering with an enzyme called EPSP synthase, which plants need to produce several critical amino acids. After many years of herbicide use, some weeds have begun to acquire genetic mutations that allow them to become resistant to those herbicides, which makes them less effective and poses a significant threat to crop yields.

“There’s a real need for new herbicides because of resistance mutations that have been arising,” Voigt says. “There are also concerns about herbicides’ possible impacts on the environment and on humans, so it’s important to identify alternative ways of making herbicides, which as a general class of molecule are really critical to agriculture.”

The MIT-led team plans to design generative AI models that will be able to predict molecules that can help prevent resistance from arising. The models will also optimize for other features such as low cost of synthesis, low risk of inducing resistance, and low risk of harming humans or causing ecological damage. 

The group developing these AI models is led by Connor Coley, the Warren K. Lewis Associate Professor of Chemical Engineering at MIT; Regina Barzilay, the School of Engineering Distinguished Professor for AI and Health at MIT; and Alexander Tropsha, the K.H. Lee Distinguished Professor in the UNC Eshelman School of Pharmacy.

The models are expected to generate billions of possible candidates, which will then be tested by a second research team. This high-throughput screening group will be led by Angela Koehler, the Charles W. and Jennifer C. Johnson Professor of Biological Engineering at MIT, and Jaime Cheah, the associate director of scientific outreach and collaborative screening in the Center for the Development of Therapeutics at the Broad Institute.

“Those libraries will be screened both to identify those molecules that hit their target, but also to then provide all of that screening data back to the computational approach in order to improve it,” Voigt says.

Once the candidate pools are narrowed down, researchers on a third team will begin greenhouse studies to evaluate how each molecule performs under different agricultural conditions. That team will be led by Neal Stewart and Scott Lenaghan, co-directors of the Center for Agricultural Synthetic Biology at UTK; Patrick Tranel, the Tom and Peggy Ainsworth Professor of Crop Sciences at the University of Illinois; Tim Willson, the Harold Kohn Distinguished Professor at the UNC Eshelman School of Pharmacy; and Andee Wallace PhD ’20, CEO and founder of Robigo. 

The first greenhouse tests of initial RNAi candidates are expected to be complete by the end of the first year of the program.

As part of this project, the research team will also develop living microbial systems that degrade agrochemicals before they reach food, soil, or waterways. Engineered bacteria, fungi, and srRNA will convert chemicals and formulation ingredients into biomass, providing a scalable strategy to reduce environmental and human exposure.

This research is funded by the Advanced Research Projects Agency for Health (ARPA-H) under agreement number 140D042690030. The views and conclusions contained in this article are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the United States government.

The United States used to be the world leader in metal casting, a process where liquid hot metal is poured into a mold to solidify into a desired shape. But more than 70 percent of American metal casting facilities have closed since the industry’s peak in the 1950s. Today, China and India dominate global metal casting production, which is critical for making the components of jet engines, nuclear and gas turbines, medical devices, and more.

Fabri is trying to revitalize American metal casting. The company, founded by Steven Davis ’24; Thomas Cole ’91, MBA ’13; and Pieter Coulier, is building a new kind of metal casting foundry that uses additive manufacturing, software, and vertical integration to eliminate many of the most labor-intensive steps associated with traditional metal casting. 

Fabri is already producing parts for a growing list of customers at its 16,000-square-foot plant in Billerica, Massachusetts, where it is applying its approach to one of the most multidisciplinary and challenging types of metal casting, known as investment casting. The company is working with Raytheon, Lockheed Martin, and Sikorsky Aircraft, among others, and is moving through qualification to make parts for the most critical aerospace and defense applications.

Fabri’s first foundry was designed to be quickly replicated. Even as it significantly expands capacity at its current plant, demand from customers is driving the company to build a far larger facility in 2027 that will produce bigger parts at higher volume as customer programs move into production.

“Metal casting is one of the oldest and most prevalent manufacturing methods in the world,” Davis says. “It’s a uniquely difficult process because there are so many different steps. There’s so much domain knowledge and experience you need to understand it. If we get cut off from the world’s casting capacity, that means no more pots and pans, no more storm drains and other basic things, but also no more aircraft. Lots of medical devices, like titanium hip implants, are cast. Power generation also depends on metal parts. These are very critical industries that we need to continue to produce.”

Faster metal production

Davis grew up competing in robotics competitions like BattleBots. The biggest challenge for his small, underfunded teams was getting the parts to make the robots.

Davis arrived at MIT in 2020, where he majored in mechanical engineering. As part of a summer internship after his sophomore year, he worked at Relativity Space, which builds reusable rockets. There he saw multi-million-dollar projects being delayed by the same hurdles he faced in robotics competitions. His team would wait weeks for expensive machines to make parts.

“I realized the problem was scale,” Davis says. “These production processes couldn’t even scale to aerospace, so they weren’t going to scale to far larger industries.”

When Davis returned to MIT that fall, he worked on adapting the precision and flexibility of 3D printing to producing metal parts. Davis also took several classes taught by John Hart, MIT’s Class of 1922 Professor and head of the Department of Mechanical Engineering. Hart co-founded several additive manufacturing companies including VulcanForms and Desktop Metal. He continues to advise Fabri.

“I was very interested in finding the best uses of additive manufacturing,” Davis recalls. “A lot of the most successful companies weren’t making end parts; they were making tools. My favorite example is Invisalign. They’re not printing the dental liners: They are printing the molds that make the aligners.”

Davis began thinking about starting a company roughly midway through his bachelor’s degree at MIT. He met Cole through his MIT network. Cole had founded a fully automated CNC machining company making dental implants that was acquired by AstraZeneca. Pieter Coulier, Fabri’s third co-founder, previously developed the simulation and metal 3D-printing algorithms at Velo3D that enabled SpaceX’s Raptor engines to be printable.

Davis also began visiting foundries, where he repeatedly heard their biggest bottleneck was labor.

“They all have huge ‘Help Wanted’ signs out front,” Davis says. “Capacity is going down. That’s why we realized automation had to be a core part of our thesis. According to the American Foundry Society, these workers are retiring at a rate of 3-to-1. It’s really hard to find these foundry engineers who understand what goes into the process and can design these molds. We have customers tell us they had an engineer retire and now they can’t make certain parts anymore. We want everything we learn to be encoded into our software forever.”

Investment casting is a complex, multistep process.

“Traditional investment casting starts by cutting a hard tool out of steel,” Davis explains. “Then you injection mold wax patterns into it. Those wax parts get glued together into what’s known as a tree. Then you dip the tree into liquid loaded with ceramic powder, you stucco it with sand, dry it, and repeat the process seven or eight times. That’s how you get the ceramic mold to pour your steel into to make the part.”

Fabri’s process starts with the part’s computer-aided design (CAD) file. The company’s proprietary software, FoundryOS, designs a pattern for each part, which Fabri prints in resin using established techniques including digital light processing (DLP) and stereolithography. The printed pattern is dipped in ceramic slurry to build the shell that molten metal is poured into. FoundryOS incorporates data from every step and uses machine learning models to improve the process and the parts with each casting.

“By printing that mold right off the bat, we remove the need for all those early steps,” Davis explains. “We fully automate the creation of the [complex patterns and channels] needed for making that tree. Instead, we print it all in one piece and consolidate a lot of that work.”

Davis says traditional tooling can cost upward of $1 million for complex enough designs and take many months to make — a big reason companies have to wait so long for parts.

Davis built the first prototype of Fabri’s printer in the basement of his MIT fraternity using funds from the MIT Sandbox Innovation Fund. After subleasing a space in Ashland, Massachusetts, Fabri was pouring metal within six months. Today, he says Fabri’s approach also cuts post processing and benefits from the increased flexibility of 3D printing, which allows Fabri to iterate on parts in days.

“What really hurts traditional foundries is they put a design into production, they tool up, they cut a tool, and when they get their first part, they realize something’s wrong,” Davis says. “Then they have to rework it or fix it. They’d like to fix the tooling in the process, but they can’t because they spent so much on the tool and waited six to nine months for it.”

Reinvigorating an industry

Fabri opened its first foundry in Ashland in 2025 and moved production to Billerica in spring of 2026. Davis says the company can go from receiving a design to producing a finished part in as little as a week. Fabri is making parts out of aluminum, copper alloys, and stainless steel, primarily for munitions, aviation, and naval applications. In early 2027, the company plans to begin casting nickel superalloys, the metals used in the turbine blades that can currently have four-year wait times.

The company’s process also enables complex geometries and organic topologies that hard tooling can’t produce.

“We’re building the software that can make these parts more intelligently and faster,” Davis says. “Qualifying for aerospace production with a company like Raytheon is a really big deal. Most startups and 3D-printing companies we know haven’t done it. The ability for us to go from opening our first facility in 2025 to now going into production with some of the nation’s most critical platforms is a huge milestone. We are going to be making parts that are going to fly in the systems and go into the munitions platforms that the country needs.”

When Andrew Voelsing was 8 years old, he decided he was going to MIT.

He was fascinated by space and dreamed of building spaceships. The Institute, and its well-known aerospace engineering program, seemed like the obvious destination. 

Years later, as a senior studying climate system science and engineering, Voelsing is preparing for a different kind of career: serving as a Navy officer, using his technical training to help the armed forces understand the ocean and atmosphere. 

Even during childhood, military service was never far from his mind. His mother attended the U.S. Naval Academy, and his father participated in the Reserve Officers’ Training Corps (ROTC) at the University of Notre Dame. The two met while serving in the Navy in Japan. Growing up, Voelsing heard their stories about military life and saw how their experiences as naval officers shaped their lives.

Voelsing was eventually accepted to the Naval Academy himself. But he chose MIT for the variety of faculty and activities he could pursue while also earning his commission as a Navy officer through the NROTC program. He will soon assume the highest-ranking student role of Battalion Commander, responsible for NROTC across Greater Boston.

But the path from an 8-year-old’s dream to his senior year in Cambridge, Massachusetts, was not quite as straightforward as he had imagined. After arriving at MIT, Voelsing struggled to find his footing academically.

“I thought I was smart in high school. I thought I was at the top of my class, and then I got here and I was failing midterms,” he says. “All my friends seemed to be getting things faster than I was, and I just felt really out of place.”

During the winter break of his first year, he talked with his father about how he was feeling. His father encouraged him to find something that combined what he was good at, what fulfilled him, and what the world needed.

The conversation helped Voelsing reconsider his academic path. He switched from aerospace engineering to climate system science and engineering (Course 1-12), a then-new major offered jointly by the departments of Civil and Environmental Engineering and of Earth, Atmospheric and Planetary Sciences, and returned to campus feeling energized by his studies. 

The change also helped him see how his education could serve a purpose beyond earning a degree. As he continued his ROTC training, he began to consider how technical knowledge about the environment could inform decisions with real-world significance.

The change, he says, was like night and day.

“I really figured out how to study,” he says. “Finding out my own vision of self and academic fulfillment helped me propel myself forward.”

A course in environmental history with Professor Susan Solomon, an atmospheric chemist who helped discover the cause of the Antarctic ozone hole, further shaped his interests. Each week, the class examined a different environmental issue, encouraging students to investigate primary sources and identify questions that had received less attention.

For his final paper, Voelsing researched an agroforestry project in Uganda. The project sparked a deeper interest in global agricultural and food systems, an area he hopes to explore in graduate school.

“There’s not going to be this one technology that comes along, and suddenly climate change isn’t an issue,” he says. “It will be a combination of 100 things making it 1 percent better for us to get across that finish line.”

Outside the classroom, one of Voelsing’s most formative experiences has come through Camp Kesem, a student-run nonprofit that provides free summer camps for children whose parents have been affected by cancer.

He joined MIT’s chapter as a first-year student. The chapter serves more than 200 campers, ages 5 to 18, giving them a week of sleepaway camp filled with activities such as ziplining, swimming, and games.

Over the years, he has taken on several leadership roles. He started as a counselor, then became operations coordinator, helping plan and run the camp. Later, he led alumni relations and organized a fundraising gala. As a senior, he returned to counseling, realizing that the part of the experience he valued most was spending time with the children.

“The thing I enjoy most isn’t all the planning; it’s not all the coordinating,” he says. “The thing I enjoy most is just spending time with kids.”

Camp Kesem also changed how he understands empathy. Before joining, he says, he was uncomfortable when other people cried. Through conversations with campers and fellow counselors, he learned the importance of sitting with someone through difficult emotions instead of trying to make those feelings go away.

“Being able to sit with a person as they grieve and experience that grief with them really changes your views on empathy and changes your views on what is actually important,” he says.

Voelsing has tried to bring that openness to his friendships on campus, too. He values conversations that allow people to understand how different experiences have shaped their perspectives, and he makes an effort to connect people who might not otherwise meet.

“I would give up a good night’s sleep seven nights a week for a good conversation or staying up talking to a friend,” he says. “And I often do.”

That same willingness to learn from others has informed his experience in ROTC. Through the program’s training and relationships with fellow midshipmen, Voelsing has gained a perspective on military service that extends beyond the family stories he grew up hearing.

He recently received his service assignment as a meteorology and oceanography officer, a specialty that will allow him to draw on his scientific background. 

Before beginning his service, however, Voelsing hopes to attend graduate school. He wants to deepen his expertise before joining the Navy full time, believing that further study will allow him to contribute more once he begins his career.

Balancing his academic work, ROTC commitments, and extracurricular activities requires him to be deliberate about how he spends his time. Rather than getting overwhelmed by everything on his plate, Voelsing tries to focus on the responsibility in front of him.   

“I’m trying to tackle it with enthusiasm, and trying to be where my feet are and do the thing well,” he says.

Looking back on his four years at MIT, Voelsing says the most important influence on him has not been a particular class or professor, but the community he has found among his friends. He values the way MIT brings together people with different interests, backgrounds, and ways of thinking, and he hopes to be someone who helps those people connect.

That sense of responsibility also informs how he thinks about the future. Whether in climate science or the military, he wants to approach difficult decisions with an awareness of their consequences and a willingness to take on challenges others might find daunting.

“Somebody’s got to do it. Why not me?” he says. “I think that is really the through line of what I want to do academically and the kind of life I want to lead.”

It was a landmark in history: In 1957, Ghana became the first sub-Saharan African state to achieve independence from colonial rule, having previously been under the control of Great Britain. 

But gaining political independence does not mean the past is left behind, and there has long been public debate about the many long-term effects of the colonial period on state and society in subsequent decades.  

Now, an empirically rich study explores one aspect of the Ghanaian state, the composition of its bureaucracy, during and after British rule. The study finds that two ethnic groups, the Ga and the Fante, which made up 14 percent of the population combined, held over 70 percent of public-sector positions in the 1950s, suggesting goods such as desirable jobs had been heavily distributed along group lines. 

“At the moment of independence, you have a state that is not meritocratic, and is highly captured by certain ethnic groups,” says MIT scholar Noah Nathan, co-author of a newly published paper detailing the study’s findings. As a result, he adds, “You can imagine the politics that creates, which are demands to equalize access to state power and state authority. And things start off on this foot of ethnic political competition over the state. That is in part a legacy of colonial state-building.”

At least in this case, “Independence in Africa wasn’t this hard break from the past,” suggests Nathan, a Ford International Professor of Political Science in MIT’s Department of Political Science. “Africans became the leaders of their countries, but the state institutions that they inherited weren’t born fresh at independence. They were carried over from the colonial period.”

The paper, “The Seeds of State Capture: Merit and Patrimonialism in the Colonial Bureaucracy,” is published in advance online form in the American Political Science Review. Nathan’s co-authors are Sarah Brierley, an associate professor at the London School of Economics and Political Science; George Kwaku Ofoso, an associate professor at the London School of Economics and Political Science; and Tingxuan Zhu PhD ’26, a recent graduate of MIT’s doctoral program in political science at MIT. 

Analyzing names

To conduct the study, the scholars dug into records from the period of British rule in Ghana, which extended from the 19th century until 1957. Using documents from the British National Archives, they examined individual-level employment records for 11,702 bureaucrats from the colonial-era civil service, building a dataset spanning from 1897 to 1955. The records included 6,879 Ghanaian civil servants and 4,805 European civil servants. The scholars’ aim was to shed light on the dynamics of civil service hiring and politics. 

British leaders at the time, and some commentators subsequently, have cast the colonial state as one built on rational, merit-based principles — what scholars call a “Weberian” bureaucracy, named after the famous German sociologist Max Weber. Scholars say that other types of bureaucracies, in contrast, display “patrimonialism,” with jobs being assigned not on merit but through patronage.

To analyze this question further, the scholars looked at 13.8 million names of registered voters from contemporary Ghana, along with localized ethnicity data from the 2010 national census. They used this to establish a baseline of the relationship between names and ethnicity in the country. Then, the scholars went back to the historical archives, and, using the names there, determined the ethnicities of past Ghanaian civil servants, from 1897 to 1955. 

Ultimately this approach showed that on the eve of independence, the Ga and Fante had managed to gain a percentage of civil service jobs that was five times greater than the percentage of the population they represented. That strongly suggests that the colonial-era bureaucracy built under British rule was not merit-based, at least in terms of overall composition. 

“Scholars have said this post-1957 independent Ghana brings with it the introduction of patrimonialism into the bureaucracy, and we’re saying this was going on long before,” Nathan explains. “It actually wasn’t meritocratic at all, and Kwame Nkrumah, the first president of Ghana, didn’t inherit some Weberian rational bureaucracy that he then destroyed. He inherited this thing rife with patrimonialism and patronage, that he then adapted. And that’s a very different interpretation of the post-independence period.”

Exams and education

To further examine the question of merit, the scholars even obtained individual-level results from Ghana’s civil service exams, then analyzed those in relation to which applicants got hired — or passed over for jobs. The results indicate little correspondence between exam results and job offers, again suggesting that ethnicity and social ties mattered more in hiring, on aggregate. 

“If you were from the right ethnic group, you were going to get a job, even if you did pretty poorly on the civil service exam,” Nathan says. “And people from disfavored ethnic groups who did much better on the exam were not getting the same positions.”

The study also rules out a seemingly logical explanation for at least part of the ethnic skew in the composition of the Ghanaian civil service — that hiring reflected levels of educational attainment, even if access to schooling itself might have reflected ethnic iniquities. But the data indicate that was not a factor. 

The scholars located every school in the country geographically, dating to the 1890s, and closely measured access to education for all ethnic groups over time. The Ga and Fante groups may have had some initial advantages in this regard, but by the 1930s, many other groups had caught up in educational attainment and still were not procuring the same relative numbers of civil services jobs through the 1950s. 

“At least in this case, that can’t explain why these two groups got favored in the hiring process,” Nathan says. “It wasn’t just a direct outcome of the fact that some parts of society had more education than others.”

As it happens, geographical proximity to civil service jobs is also not an explanatory factor; the scholars also examined the locations of government offices and found they cannot account for the quantity of jobs going to the Ga and Fante. 

Out of sight

Instead, the researchers believe the likeliest scenario is that, once established in the civil service, people from the Ga and Fante managed to use their social connections to get more and more jobs offered to others from their own groups. Moreover, they may have done so not necessarily with explicit British approval, but with something more resembling indifference on the part of the colonial rulers. 

“The mechanics of the hiring process were very decentralized,” Nathan says. “And the British didn’t know very much about the society they were governing. That created an information asymmetry where local bureaucrats could get friends into jobs in a way their superiors were either not aware of or didn’t have very much incentive to stop.”

All told, Nathan observes, “The ministry of finance in Ghana in 1957 isn’t some new thing that’s created from scratch. It’s the colonial ministry of finance that’s been in place for 60 years, and the bureaucrats in it showed up to work the next day in the same jobs. [The new leaders] inherited thousands of employees who had gotten there through this process.”

The study is one of several that Nathan has published using data to answer questions that span both political science and history. Quantifying the issue in a granular way helps illuminate the large-scale political dynamics.

“People have written about this in broad strokes, even the fact that certain ethnic groups in some countries got more access to positions than others, but they haven’t had detailed microdata on exactly who got which job and when,” Nathan says. “That’s the innovation of our paper … we could unpack the nuts and bolts of how this hiring process happened, and in doing so dispel some conventional wisdom about how African states were built in the colonial period.”

The new paper is not the only piece of scholarship taking this kind of data-intensive approach to studying colonial state-building and its legacy. Ada Johnson-Kanu, an assistant professor of political science at the University of Kentucky, has conducted this sort of study of Nigeria’s bureaucracy over time, in work she completed in 2021. 

Nathan, for his part, says he hopes that other scholars can expand upon this kind of data-driven approach to digging into certain historical questions. 

“There is just incredibly rich archival evidence out there in the world for a lot of countries,” Nathan observes. “I think this is a really fun way to do research.”

Today, a scientist who wants to simulate a physical process must pick the right tool for that one job, such as a solver built for flowing water or another designed to model the way a protein folds. Each one is hand-coded around the specific equations that dictate how that process behaves.

Researchers at MIT, the University of British Columbia (UBC), and elsewhere have now replaced that patchwork with a single deep-learning system, called WorldParticle, that could help engineers model different physical phenomena, from sliding sand to folding proteins, with one unified artificial intelligence architecture.

Scientists and engineers could use this system to model processes that typically require separate simulators, such as a fluid sloshing against a deformable solid. This could offer a faster, more accurate way to simulate how sloshing oil might impact an elastic valve in a storage tank, for instance. It could also help engineers study where and how granular materials, like sand or soil, could move when a soft robot touches them.

The system represents every material it simulates, from a dune of sand to a protein molecule, as tiny particles and predicts where those particles will move one step at a time. Because it learns the underlying dynamics rather than being programmed with an equation, the model can adapt to materials, forces, and environments it never encountered during training.  

In the long term, the researchers hope this work helps scientists discover new materials and explore physical processes they would be unable to effectively study with existing simulation methods.

“We want to have a GPT moment for simulation,” says Peter Yichen Chen, a former MIT postdoc who is now an assistant professor at UBC, and is a co-corresponding author of an open-access paper on this technique. “Every time people want to build a simulator for a particular physical phenomenon, they need to code it up from scratch. Our vision is to have a foundation model that works out of the box for any scenario you want to simulate. This is a first step in that direction.”

Chen is joined by co-corresponding and co-lead author Minghao Guo, an MIT postdoc; co-lead authors Caoliwen Wang and Siyuan Chen, UBC graduate students; Wojciech Matusik, an MIT professor of electrical engineering and computer science who leads the Computational Design and Fabrication Group within the Computer Science and Artificial Intelligence Laboratory (CSAIL); and others at UBC, Georgia Tech, Inria, Meta, the University of Pennsylvania, the University of Utah, the University of California at Los Angeles, and the University of Hong Kong. The research will be presented at the ACM SIGGRAPH Conference and Exhibition on Computer Graphics and Interactive Techniques in Asia.

Unified simulation

For this work, the researchers drew inspiration from world models. These powerful AI systems learn the rules that govern the physical world so they can predict how objects or environments will change over time in response to certain actions or conditions.

The researchers applied these concepts to develop a unified AI architecture that can simulate multiple types of physical phenomena. 

“If you study deformable objects and fluids, you would need two simulators — one for each physical regime. But if you unify them into one model, maybe you could couple the physics of these two processes and gain insights into new problems?” Guo says.

WorldParticle breaks an object into thousands or millions of tiny, three-dimensional dots, or particles, using what is known as a Lagrangian representation. It tracks the movement and velocity of the particles over space and time to simulate the material’s dynamics.

Typically, when scientists model materials and physical processes using Lagrangian particles, they use a governing equation that defines the fundamental laws which control how that material behaves over time and space. They must convert that complex equation into a formula a numerical solver can handle, repeating these steps for each material and process they want to model.

Instead, the researchers designed a neural network that learns to solve governing equations in general, eliminating the need to design a new model and solver to handle each one. 

Once WorldParticle converts an object into particles, a neural network takes the 3D positions and physical properties of the particles as inputs. It outputs their positions and properties for the next frame of the simulation, predicting the material’s motion over time and space in one-second increments. 

“It is a similar architecture to ChatGPT. But instead of predicting one word at a time to write a piece of text, we are predicting how these particles will move, one step at a time,” Chen says.

Before each simulation step, WorldParticle separates the external forces that affect the physical system, like impulse forces that affect an ocean wave when it slams into a seawall. 

After predicting the initial states of the particles, WorldParticle integrates the external forces back into the simulation, correcting its particle predictions based on how those forces affect the object’s behavior. In this way, the system can generalize to unseen materials, changing environmental conditions, and varied forces.

A general, scalable framework

But because a complex object could be broken into millions of particles, the problem quickly becomes too computationally intensive for the model to handle. 

To streamline processing, the researchers employed a merging technique to combine and compress particles while still allowing WorldParticle to capture fine-grained details of their behavior.

“We needed to do a lot of detailed engineering and employ many tweaks to the architecture for it to be truly generalizable and scalable,” Guo says.

They demonstrated WorldParticle by modeling six different physical phenomena, including deformable objects, granular materials, and proteins, with higher accuracy than numerical simulation methods. 

For instance, WorldParticle effectively captured how a granular object would collapse into a pile while some baseline methods left sand particles floating in the air. Their method also modeled how protein structures change over time about 100 times faster than numerical simulators, Guo adds. 

The general-purpose framework could be applied to many materials and processes, including soft robot manipulation tasks, RNA evolution, and astrophysical phenomena like the merging of galaxies, the researchers say.

Moving forward, they want to adapt WorldParticle to simulate these and other physical systems. They also want to build on this work toward the development of a full, general-purpose simulation pipeline for scientific discovery. 

 “We see the unification of computation and science giving us new insights and tools to discover physical phenomena or explain some unexplainable physical phenomena,” Guo says. “With a unified model, we could unlock many discoveries that we can’t even imagine.”

Humans are intuitive by nature. We can sense meaning beyond the literal words that we encounter. Now, a new artificial intelligence-assisted learning platform developed by MIT engineers is enabling robots to read larger meaning into a human’s natural language commands. 

In a study to be presented at the annual Conference on Robot Learning this month, researchers from MIT and New York University introduce FLEX, a flexible feature-editing pipeline for robot motion-planning. 

With FLEX, humans can interact with a robot by giving it instructions in natural, conversational language. The robot can not only follow the instructions literally but also read into the words to draw larger generalizations that help to improve its motion-planning task. 

The team demonstrated FLEX in several simulated scenarios, including one in which a robot serves a cup of coffee. In these virtual scenarios, a person interacts with a robotic arm by typing commands in natural language. As the robot moves the coffee cup over a cluttered table, the person might type “Don’t spill on the laptop!” In response, the robot will reason about why it should avoid the laptop. For this, FLEX incorporates a large language model (LLM) — a type of AI system that is trained on enormous libraries of text, including social media posts, video transcriptions, and web content, which the LLM uses to identify larger trends, patterns, and generalizations. 

“LLMs have seen so many texts from the internet,” says study author Nathan Dennler, a postdoc in MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL). “People might have written about, ‘Oh, my phone dropped in the water and now it doesn’t work,’ or ‘I spilled coffee on my computer and now it doesn’t work.’ So LLMs pick up on the idea that liquids plus devices leads to things not working.”

Through the LLM, the robot reasons that it should also avoid bumping into all electronics. In its updated path, the robot adjusts to avoid both the laptop and a nearby phone, without the person having to explicitly spell this out.

“The idea is that there is some higher-level intention behind the words that you say,” Dennler says. “People naturally get this. But you have to carefully explain that to a robot, which takes time and effort. FLEX gives you an interface of communication with a robot to say, ‘When people say this, they mean that.’”

The study was led by Andreea Bobu, assistant professor of aeronautics and astronautics at MIT. Co-authors are Audrey Lee SM ’26 and Ilia Sucholutsky of New York University.

Beyond what we say

At MIT, Bobu heads up the Collaborative Learning and Autonomy Research (CLEAR) Lab, where her group develops autonomous agents that learn to work for, with, and around people. She is focused on developing assistive and household robots that can carry out physical tasks, and reason and adapt in real-time, in response to a person’s natural, conversational commands. 

“Robots use math to do their trajectory planning. But we can’t expect a consumer to learn to do this math in order to tell a robot what to do,” says Dennler, who is a member of the CLEAR Lab. “Instead of having to get people to learn how to use a robot in a specific way, the goal is to interact with the robot through natural language.”

There is a large effort in the robotics community to incorporate natural language into robotic learning pipelines. Various strategies include using language to clarify a task’s goal, redirect a robot’s trajectory, and update its understanding of what is a safe path to take. These existing methods use natural language to tell the robot how to physically perform a task. But a robot could potentially learn much more from what we tell it. 

Bobu and her colleagues are expanding what robots can learn from natural language commands. In their new study, they focused on enabling a robot to interpret a person’s underlying, unspecified preference from limited natural language commands.  

Absorbing common sense

The team’s new robot learning platform, FLEX, is a three-step process that helps a robot read more into a natural language command, beyond what it is explicitly told to do. 

The first step, “Add,” instructs a robot to add a feature to its list of things in its environment that matter for the task at hand, based on a user’s natural language command. For instance, as a robot is moving a bowling ball from one location to another, a person might interject with “Don’t drop it on my mug!” The robot would then add the mug to a list of things to avoid in its path. This first step is how previous methods have worked. Every time a person makes a correction, the robot adds to its features list, and updates its trajectory to avoid those features. 

“The problem happens when your feature list becomes super long, and some features are no longer relevant because your environment may have changed,” says Dennler, who notes that this approach can grow tiresome if a person has to specify every specific feature to avoid. 

The second step, “Generalize,” is the new solution that FLEX provides. This step incorporates an LLM — in this case, GPT-4o — that considers the mug, along with any other features that the person has so far specified to avoid. The LLM automatically searches through its vast cache of data for patterns and connections among these features, to ultimately make a generalization that it should avoid objects that are fragile, even when the person has not explicitly said so. 

“LLMs have seen so many texts from the internet, from all sorts of people and places,” Dennler says. “Because they’ve trained so much on those things, some of that common sense gets absorbed into the LLM.”

In the third step, “Mask,” the robot’s standard computer vision system assesses its environment to determine what objects are no longer present, and then deletes those objects from its features list. This entire three-step process takes about five seconds. 

The team tested FLEX in simulations of a robot arm carrying out three different tasks: serving a cup of coffee, moving a candle, and carrying a bowling ball. In all three scenarios, volunteers interacted with the virtual robot by typing natural language commands, such as “Don’t spill on my laptop,” “Stay away from the stack of paper,” and “Don’t drop on the mug.” With FLEX, the robot followed all three commands literally and also reasoned at a higher level to not spill on electronics, stay away from flammable objects, and not drop its cargo on fragile things. 

“Everyone’s homes are different, and everyone cares about different things, so no robot comes out of the box knowing what matters to you,” Bobu says. “What excites me about FLEX is that it can pick that up from a sentence or two instead of asking you to spell out every rule — and that’s the difference between a robot people put up with and one they actually want in their home.” 

This work was partly supported by the MIT Generative AI Impact Consortium and the MIT Schwarzman College of Computing’s Social and Ethical Responsibilities of Computing program.