Improving the workplace of the future

Whitney Zhang ’21 believes in the importance of valuing workers regardless of where they fit into an organizational chart.

Zhang is a PhD student in MIT’s Department of Economics studying labor economics. She explores how the technological and managerial decisions companies make affect workers across the pay spectrum. 

“I’ve been interested in economics, economic impacts, and related social issues for a long time,” says Zhang, who majored in mathematical economics as an undergraduate. “I wanted to apply my math skills to see how we could improve policies and their effects.”

Zhang is interested in how to improve conditions for workers. She believes it’s important to build relationships with policymakers, focusing on an evidence-driven approach to policy, while always remembering to center those the policies may affect. “We have to remember the people whose lives are impacted by business operations and legislation,” she says. 

She’s also aware of the complex intermixture of politics, social status, and financial obligations organizations and their employees have to navigate.

“Though I’m studying workers, it’s important to consider the entire complex ecosystem when solving for these kinds of challenges, including firm incentives and global economic conditions,” she says.

The intersection of tech and labor policy

Zhang began investigating employee productivity, artificial intelligence, and related economic and labor market phenomena early in her time as a doctoral student, collaborating frequently with fellow PhD students in the department.

A collaboration with economics doctoral student Shakked Noy yielded the 2023 study investigating ChatGPT as a tool to improve productivity. Their research found it substantially increased workers’ productivity on writing tasks, most so for workers who initially performed the worst on the tasks.

“This was one of the earliest pieces of evidence on the productivity effects of generative AI, and contributed to providing concrete data on how impactful these types of tools might be in the workplace and on the labor market,” Zhang says.

In other ongoing research — “Determinants of Irregular Worker Schedules” — Zhang is using data from a payroll provider to examine scheduling unpredictability, investigating why companies employ unpredictable schedules and how these schedules affect low-wage employees’ quality of life.

The scheduling project, conducted with MIT economics PhD student Nathan Lazarus, is motivated, in part, by existing sociological evidence that low-wage workers’ unpredictable schedules are associated with worse sleep and well-being. “We’ve seen a relationship between higher turnover and inconsistent, inadequate schedules, which suggests workers dis-prefer these kinds of schedules,” Zhang says.

At an academic roundtable, Zhang presented her results to Starbucks employees involved in scheduling and staffing. The attendees wanted to learn more about how different scheduling practices impacted workers and their productivity. “These are the kinds of questions that could reveal useful information for small businesses, large corporations, and others,” she says.

By conducting this research, Zhang hopes to better understand whether or not scheduling regulations can improve affected employees’ quality of life, while also considering potential unintended consequences. “Why are these schedules set the way they’re set?” she asks. “Do businesses with these kinds of schedules require increased regulation?”

Another project, conducted with MIT economics doctoral student Arjun Ramani, examines the linkages between offshoring, remote work, and related outcomes. “Do the technological and managerial practices that have made remote work possible further facilitate offshoring?” she asks. “Do organizations see significant gains in efficiency? What are the impacts on U.S. and offshore workers?”

Her work is being funded through the National Science Foundation Graduate Research Fellowship Program and the Washington Center for Equitable Growth.

Putting people at the center

Zhang has observed the different kinds of people economics and higher education could bring together. She followed a dual enrollment track in high school, completing college-level courses with students from across a variety of demographic identities. “I enjoyed centering people in my work,” she says. “Taking classes with a diverse group of students, including veterans and mothers returning to school to complete their studies, made me more curious about socioeconomic issues and the policies relevant to them.”

She later enrolled at MIT, where she participated in the Undergraduate Research Opportunities Program (UROP). She also completed an internship at the World Bank, worked as a summer analyst at the Federal Reserve Bank of New York, and worked as an assistant for a diverse faculty cohort including MIT economists David Autor, Jon Gruber, and Nina Roussille. Autor is her primary advisor on her doctoral research, a mentor she cites as a significant influence.

“[Autor’s] course, 14.03 (Microeconomics and Public Policy), cemented connections between theory and practice,” she says. “I thought the class was revelatory in showing the kinds of questions economics can answer.”

Doctoral study has revealed interesting pathways of investigation for Zhang, as have her relationships with her student peers and other faculty. She has, for example, leveraged faculty connections to gain access to hourly wage data in support of her scheduling and employee impacts work. “Generally, economists have had administrative data on earnings, but not on hours,” she notes.

Zhang’s focus on improving others’ lives extends to her work outside the classroom. She’s a mentor for the Boston Chinatown Neighborhood Center College Access Program and a member of MIT’s Graduate Christian Fellowship group. When she’s not enjoying spicy soups or paddling on the Charles, she takes advantage of opportunities to decompress with her art at W20 Arts Studios.

“I wanted to create time for myself outside of research and the classroom,” she says.

Zhang cites the benefits of MIT’s focus on cross-collaboration and encouraging students to explore other disciplines. As an undergraduate, Zhang minored in computer science, which taught her coding skills critical to her data work. Exposure to engineering also led her to become more interested in questions around how technology and workers interact.

Working with other scholars in the department has improved how Zhang conducts inquiries. “I’ve become the kind of well-rounded student and professional who can identify and quantify impacts, which is invaluable for future projects,” she says. Exposure to different academic and research areas, Zhang argues, helps increase access to ideas and information.

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MIT engineers develop a magnetic transistor for more energy-efficient electronics

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.

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NASA selects Adam Fuhrmann ’11 for astronaut training

U.S. Air Force Maj. Adam Fuhrmann ’11 was one of 10 individuals chosen from a field of 8,000 applicants for the 2025 U.S. astronaut candidate class, NASA announced in a live ceremony on Sept. 22. 

This is NASA’s 24th class of astronaut candidates since the first Mercury 7 astronauts were chosen in 1959. Upon completion of his training, Fuhrmann will become the 45th MIT graduate to become a flight-eligible astronaut.

“As test pilots we don't do anything on our own, we work with amazing teams of engineers and maintenance professionals to plan, simulate, and execute complex and sometimes risky missions in aircraft to collect data and accomplish a mission, all while assessing risk and making smart calls as a team to do that as safely as possible,” Fuhrmann said at NASA’s announcement ceremony in Houston, Texas. “I'm happy to try to bring some of that experience to do the same thing with the NASA team and learn from everyone at Johnson Space Center how to apply those lessons to human spaceflight.”

His class now begins two years of training at the Johnson Space Center in Houston that includes instruction and skills development for complex operations aboard the International Space Station, Artemis missions to the moon, and beyond. Training includes robotics, land and water survival, geology, foreign language, space medicine and physiology, and more, while also conducting simulated spacewalks and flying high-performance jets.

From MIT to astronaut training

Fuhrmann, 35, is from Leesburg, Virginia, and has accumulated more than 2,100 flight hours in 27 aircraft, including the F-16 and F-35. He has served as a U.S. Air Force fighter pilot and experimental test pilot for nearly 14 years and deployed in support of operations Freedom’s Sentinel and Resolute Support, logging 400 combat hours.

Fuhrmann holds a bachelor’s degree in aeronautics and astronautics from MIT and master’s degrees in flight test engineering and systems engineering from the U.S. Air Force Test Pilot School and Purdue University, respectively. While at MIT, he was a member of Air Force ROTC Detachment 365 and was selected as the third-ever student leader of the Bernard M. Gordon-MIT Engineering Leadership Program (GEL) in spring 2011.

“We are tremendously proud of Adam for this notable accomplishment, and we look forward to following his journey through astronaut candidate school and beyond,” says Leo McGonagle, GEL founding and executive director.

“It’s always a thrill to learn that one of our own has joined NASA's illustrious astronaut corps,” says Julie Shah, head of the MIT Department of Aeronautics and Astronautics and the H.N. Slater Professor in Aeronautics and Astronautics. “Adam is Course 16’s 19th astronaut alum. We take very seriously the responsibility to provide the very best aerospace engineering education, and it's so gratifying to see that those fundamentals continue to set individuals from our community on the path to becoming an astronaut.”

Learning to be a leader at MIT

McGonagle recalls that Fuhrmann was a very early participant in GEL from 2009 to 2011.

“The GEL Program was still in its infancy during this time and was in somewhat of a fragile state as we were seeking to grow and cement ourselves as a viable MIT program. As the fall 2010 semester was winding down, it was evident that the program needed an effective GEL2 student leader during the spring semester, who could lead by example and inspire fellow students and who was an example of what right looks like. I knew Adam was already an emerging leader as a senior cadet in MIT’s Air Force ROTC Detachment, so I tapped him for the role of spring student leader of GEL,” said McGonagle.

Fuhrmann initially sought to decline this role, citing his time as a leader in ROTC. But McGonagle, having led the Army ROTC Program prior to GEL, felt that the GEL Student Leader role would challenge and develop Fuhrmann in other ways. In GEL, he would be charged with leading and inspiring students from a broad background of experiences, and focused exclusively on leading within engineering contexts, while engaging with engineering industry organizations.

“GEL needed strong student leadership at this time, so Adam took on the role, and it ended up being a win-win for both him and the program. He later expressed to me that the experience challenged him in ways that he hadn’t anticipated and complemented his Air Force ROTC leadership development. He was grateful for the opportunity, and the program stabilized and grew under Adam’s leadership. He was the right student at the right time and place,” said McGonagle.

Fuhrmann has remained connected to the GEL program. He asked McGonagle to administer his oath of commissioning into the U.S. Air Force, with his family in attendance, at the historic Bunker Hill Monument in Boston. “One of my proudest GEL memories,” said McGonagle, who is a former U.S. Army Lt. Colonel.

Throughout his time in service which included overseas deployments, Fuhrmann has actively participated in Junior Engineering Leader’s Roundtable leadership labs (ELLs) with GEL students, and he has kept in touch with his GEL2 cohort.

“Adam’s GEL2 cohort meets informally once or twice a year, usually via Zoom, to share and discuss professional challenges, lessons learned, life stories, to keep in touch with each other. This small but excellent group of GEL alum is committed to staying connected and supporting one another, as part of the broader GEL community,” said McGonagle.

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MIT’s work with Idaho National Laboratory advances America’s nuclear industry

At the center of nuclear reactors across the United States, a new type of chromium-coated fuel is being used to make the reactors more efficient and more resistant to accidents. The fuel is one of many innovations sprung from collaboration between researchers at MIT and the Idaho National Laboratory (INL) — a relationship that has altered the trajectory of the country’s nuclear industry.

Amid renewed excitement around nuclear energy in America, MIT’s research community is working to further develop next-generation fuels, accelerate the deployment of small modular reactors (SMRs), and enable the first nuclear reactor in space.

Researchers at MIT and INL have worked closely for decades, and the collaboration takes many forms, including joint research efforts, student and postdoc internships, and a standing agreement that lets INL employees spend extended periods on MIT’s campus researching and teaching classes. MIT is also a founding member of the Battelle Energy Alliance, which has managed the Idaho National Laboratory for the Department of Energy since 2005.

The collaboration gives MIT’s community a chance to work on the biggest problems facing America’s nuclear industry while bolstering INL’s research infrastructure.

“The Idaho National Laboratory is the lead lab for nuclear energy technology in the United States today — that’s why it’s essential that MIT works hand in hand with INL,” says Jacopo Buongiorno, the Battelle Energy Alliance Professor in Nuclear Science and Engineering at MIT. “Countless MIT students and postdocs have interned at INL over the years, and a memorandum of understanding that strengthened the collaboration between MIT and INL in 2019 has been extended twice.”

Ian Waitz, MIT’s vice president for research, adds, “The strong collaborative history between MIT and the Idaho National Laboratory enables us to jointly contribute practical technologies to enable the growth of clean, safe nuclear energy. It’s a clear example of how rigorous collaboration across sectors, and among the nation’s top research facilities, can advance U.S. economic prosperity, health, and well-being.”

Research with impact

Much of MIT’s joint research with INL involves tests and simulations of new nuclear materials, fuels, and instrumentation. One of the largest collaborations was part of a global push for more accident-tolerant fuels in the wake of the nuclear accident that followed the 2011 earthquake and tsunami in Fukushima, Japan.

In a series of studies involving INL and members of the nuclear energy industry, MIT researchers helped identify and evaluate alloy materials that could be deployed in the near term to not only bolster safety but also offer higher densities of fuel.

“These new alloys can withstand much more challenging conditions during abnormal occurrences without reacting chemically with steam, which could result in hydrogen explosions during accidents,” explains Buongiorno, who is also the director of science and technology at MIT’s Nuclear Reactor Laboratory and the director of MIT’s Center for Advanced Nuclear Energy Systems. “The fuels can take much more abuse without breaking apart in the reactor, resulting in a higher safety margin.”

The fuels tested at MIT were eventually adopted by power plants across the U.S., starting with the Byron Clean Energy Center in Ogle County, Illinois.

“We’re also developing new materials, fuels, and instrumentation,” Buongiorno says. “People don’t just come to MIT and say, ‘I have this idea, evaluate it for me.’ We collaborate with industry and national labs to develop the new ideas together, and then we put them to the test,  reproducing the environment in which these materials and fuels would operate in commercial power reactors. That capability is quite unique.”

Another major collaboration was led by Koroush Shirvan, MIT’s Atlantic Richfield Career Development Professor in Energy Studies. Shirvan’s team analyzed the costs associated with different reactor designs, eventually developing an open-source tool to help industry leaders evaluate the feasibility of different approaches.

“The reason we’re not building a single nuclear reactor in the U.S. right now is cost and financial risk,” Shirvan says. “The projects have gone over budget by a factor of two and their schedule has lengthened by a factor of 1.5, so we’ve been doing a lot of work assessing the risk drivers. There’s also a lot of different types of reactors proposed, so we’ve looked at their cost potential as well and how those costs change if you can mass manufacture them.”

Other INL-supported research of Shirvan’s involves exploring new manufacturing methods for nuclear fuels and testing materials for use in a nuclear reactor on the surface of the moon.

“You want materials that are lightweight for these nuclear reactors because you have to send them to space, but there isn’t much data around how those light materials perform in nuclear environments,” Shirvan says.

People and progress

Every summer, MIT students at every level travel to Idaho to conduct research in INL labs as interns.

“It’s an example of our students getting access to cutting-edge research facilities,” Shirvan says.

There are also several joint research appointments between the institutions. One such appointment is held by Sacit Cetiner, a distinguished scientist at INL who also currently runs the MIT and INL Joint Center for Reactor Instrumentation and Sensor Physics (CRISP) at MIT’s Nuclear Reactor Laboratory.

CRISP focuses its research on key technology areas in the field of instrumentation and controls, which have long stymied the bottom line of nuclear power generation.

“For the current light-water reactor fleet, operations and maintenance expenditures constitute a sizeable fraction of unit electricity generation cost,” says Cetiner. “In order to make advanced reactors economically competitive, it’s much more reasonable to address anticipated operational issues during the design phase. One such critical technology area is remote and autonomous operations. Working directly with INL, which manages the projects for the design and testing of several advanced reactors under a number of federal programs, gives our students, faculty, and researchers opportunities to make a real impact.”

The sharing of experts helps strengthen MIT and the nation’s nuclear workforce overall.

“MIT has a crucial role to play in advancing the country’s nuclear industry, whether that’s testing and developing new technologies or assessing the economic feasibility of new nuclear designs,” Buongiorno says.

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MIT named No. 2 university by U.S. News for 2025-26

MIT has placed second in U.S. News and World Report’s annual rankings of the nation’s best universities, announced today. 

As in past years, MIT’s engineering program continues to lead the list of undergraduate engineering programs at a doctoral institution. The Institute also placed first in five out of 10 engineering disciplines.

U.S. News placed MIT first in its evaluation of undergraduate computer science programs, ranking it No. 1 in four out of 10 computer science disciplines.

MIT also topped the list of undergraduate business programs, a ranking it shares with the University of Pennsylvania. Among business subfields, MIT is ranked first in two out of 10 specialties.

Within the magazine’s rankings of “academic programs to look for,” MIT topped the list in the category of undergraduate research and creative projects. The Institute also ranks as the second most innovative national university and the fourth best value, according to the U.S. News peer assessment survey of top academics.

MIT placed first in five engineering specialties: aerospace/aeronautical/astronautical engineering; chemical engineering; computer engineering; materials engineering; and mechanical engineering. It placed within the top five in two other engineering areas: biomedical engineering and electrical/electronic/communication engineering.

Other schools in the top five overall for undergraduate engineering programs are Stanford University, the University of California at Berkeley, Georgia Tech, Caltech, the University of Illinois at Urbana-Champaign, and the University of Michigan at Ann Arbor.

In computer science, MIT placed first in four specialties: artificial intelligence (shared with Carnegie Mellon University); biocomputing/bioinformatics/biotechnology; computer systems; and theory. It placed in the top five of six other disciplines: cybersecurity; data analytics/science; game/simulation development (shared with Carnegie Mellon); mobile/web applications; programming languages; and software engineering.

Other schools in the top five overall for undergraduate computer science programs are Carnegie Mellon, Stanford, UC Berkeley, Princeton University, and Georgia Tech.

Among undergraduate business specialties, the MIT Sloan School of Management leads in production/operations management and quantitative analysis. It also placed within the top five in five other categories: analytics; entrepreneurship; finance; management information systems; and supply chain management/logistics.

Other undergraduate business programs ranking in the top five include UC Berkeley, the University of Michigan at Ann Arbor, and New York University.

Recently, U.S. News & World Report ranked medium to large undergraduate economics programs based on a peer assessment survey; MIT’s economics program has placed first in this ranking.

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MIT affiliates win AI for Math grants to accelerate mathematical discovery

MIT Department of Mathematics researchers David Roe ’06 and Andrew Sutherland ’90, PhD ’07 are among the inaugural recipients of the Renaissance Philanthropy and XTX Markets’ AI for Math grants. 

Four additional MIT alumni — Anshula Gandhi ’19, Viktor Kunčak SM ’01, PhD ’07; Gireeja Ranade ’07; and Damiano Testa PhD ’05 — were also honored for separate projects.

The first 29 winning projects will support mathematicians and researchers at universities and organizations working to develop artificial intelligence systems that help advance mathematical discovery and research across several key tasks.

Roe and Sutherland, along with Chris Birkbeck of the University of East Anglia, will use their grant to boost automated theorem proving by building connections between the L-Functions and Modular Forms Database (LMFDB) and the Lean4 mathematics library (mathlib).

“Automated theorem provers are quite technically involved, but their development is under-resourced,” says Sutherland. With AI technologies such as large language models (LLMs), the barrier to entry for these formal tools is dropping rapidly, making formal verification frameworks accessible to working mathematicians. 

Mathlib is a large, community-driven mathematical library for the Lean theorem prover, a formal system that verifies the correctness of every step in a proof. Mathlib currently contains on the order of 10⁵ mathematical results (such as lemmas, propositions, and theorems). The LMFDB, a massive, collaborative online resource that serves as a kind of “encyclopedia” of modern number theory, contains more than 10⁹ concrete statements. Sutherland and Roe are managing editors of the LMFDB.

Roe and Sutherland’s grant will be used for a project that aims to augment both systems, making the LMFDB’s results available within mathlib as assertions that have not yet been formally proved, and providing precise formal definitions of the numerical data stored within the LMFDB. This bridge will benefit both human mathematicians and AI agents, and provide a framework for connecting other mathematical databases to formal theorem-proving systems.

The main obstacles to automating mathematical discovery and proof are the limited amount of formalized math knowledge, the high cost of formalizing complex results, and the gap between what is computationally accessible and what is feasible to formalize.

To address these obstacles, the researchers will use the funding to build tools for accessing the LMFDB from mathlib, making a large database of unformalized mathematical knowledge accessible to a formal proof system. This approach enables proof assistants to identify specific targets for formalization without the need to formalize the entire LMFDB corpus in advance.

“Making a large database of unformalized number-theoretic facts available within mathlib will provide a powerful technique for mathematical discovery, because the set of facts an agent might wish to consider while searching for a theorem or proof is exponentially larger than the set of facts that eventually need to be formalized in actually proving the theorem,” says Roe.

The researchers note that proving new theorems at the frontier of mathematical knowledge often involves steps that rely on a nontrivial computation. For example, Andrew Wiles’ proof of Fermat’s Last Theorem uses what is known as the “3-5 trick” at a crucial point in the proof.

“This trick depends on the fact that the modular curve X_0(15) has only finitely many rational points, and none of those rational points correspond to a semi-stable elliptic curve,” according to Sutherland. “This fact was known well before Wiles’ work, and is easy to verify using computational tools available in modern computer algebra systems, but it is not something one can realistically prove using pencil and paper, nor is it necessarily easy to formalize.”

While formal theorem provers are being connected to computer algebra systems for more efficient verification, tapping into computational outputs in existing mathematical databases offers several other benefits.

Using stored results leverages the thousands of CPU-years of computation time already spent in creating the LMFDB, saving money that would be needed to redo these computations. Having precomputed information available also makes it feasible to search for examples or counterexamples without knowing ahead of time how broad the search can be. In addition, mathematical databases are curated repositories, not simply a random collection of facts. 

“The fact that number theorists emphasized the role of the conductor in databases of elliptic curves has already proved to be crucial to one notable mathematical discovery made using machine learning tools: murmurations,” says Sutherland.

“Our next steps are to build a team, engage with both the LMFDB and mathlib communities, start to formalize the definitions that underpin the elliptic curve, number field, and modular form sections of the LMFDB, and make it possible to run LMFDB searches from within mathlib,” says Roe. “If you are an MIT student interested in getting involved, feel free to reach out!” 

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New tool makes generative AI models more likely to create breakthrough materials

The artificial intelligence models that turn text into images are also useful for generating new materials. Over the last few years, generative materials models from companies like Google, Microsoft, and Meta have drawn on their training data to help researchers design tens of millions of new materials.

But when it comes to designing materials with exotic quantum properties like superconductivity or unique magnetic states, those models struggle. That’s too bad, because humans could use the help. For example, after a decade of research into a class of materials that could revolutionize quantum computing, called quantum spin liquids, only a dozen material candidates have been identified. The bottleneck means there are fewer materials to serve as the basis for technological breakthroughs.

Now, MIT researchers have developed a technique that lets popular generative materials models create promising quantum materials by following specific design rules. The rules, or constraints, steer models to create materials with unique structures that give rise to quantum properties.

“The models from these large companies generate materials optimized for stability,” says Mingda Li, MIT’s Class of 1947 Career Development Professor. “Our perspective is that’s not usually how materials science advances. We don’t need 10 million new materials to change the world. We just need one really good material.”

The approach is described today in a paper published by Nature Materials. The researchers applied their technique to generate millions of candidate materials consisting of geometric lattice structures associated with quantum properties. From that pool, they synthesized two actual materials with exotic magnetic traits.

“People in the quantum community really care about these geometric constraints, like the Kagome lattices that are two overlapping, upside-down triangles. We created materials with Kagome lattices because those materials can mimic the behavior of rare earth elements, so they are of high technical importance.” Li says.

Li is the senior author of the paper. His MIT co-authors include PhD students Ryotaro Okabe, Mouyang Cheng, Abhijatmedhi Chotrattanapituk, and Denisse Cordova Carrizales; postdoc Manasi Mandal; undergraduate researchers Kiran Mak and Bowen Yu; visiting scholar Nguyen Tuan Hung; Xiang Fu ’22, PhD ’24; and professor of electrical engineering and computer science Tommi Jaakkola, who is an affiliate of the Computer Science and Artificial Intelligence Laboratory (CSAIL) and Institute for Data, Systems, and Society. Additional co-authors include Yao Wang of Emory University, Weiwei Xie of Michigan State University, YQ Cheng of Oak Ridge National Laboratory, and Robert Cava of Princeton University.

Steering models toward impact

A material’s properties are determined by its structure, and quantum materials are no different. Certain atomic structures are more likely to give rise to exotic quantum properties than others. For instance, square lattices can serve as a platform for high-temperature superconductors, while other shapes known as Kagome and Lieb lattices can support the creation of materials that could be useful for quantum computing.

To help a popular class of generative models known as a diffusion models produce materials that conform to particular geometric patterns, the researchers created SCIGEN (short for Structural Constraint Integration in GENerative model). SCIGEN is a computer code that ensures diffusion models adhere to user-defined constraints at each iterative generation step. With SCIGEN, users can give any generative AI diffusion model geometric structural rules to follow as it generates materials.

AI diffusion models work by sampling from their training dataset to generate structures that reflect the distribution of structures found in the dataset. SCIGEN blocks generations that don’t align with the structural rules.

To test SCIGEN, the researchers applied it to a popular AI materials generation model known as DiffCSP. They had the SCIGEN-equipped model generate materials with unique geometric patterns known as Archimedean lattices, which are collections of 2D lattice tilings of different polygons. Archimedean lattices can lead to a range of quantum phenomena and have been the focus of much research.

“Archimedean lattices give rise to quantum spin liquids and so-called flat bands, which can mimic the properties of rare earths without rare earth elements, so they are extremely important,” says Cheng, a co-corresponding author of the work. “Other Archimedean lattice materials have large pores that could be used for carbon capture and other applications, so it’s a collection of special materials. In some cases, there are no known materials with that lattice, so I think it will be really interesting to find the first material that fits in that lattice.”

The model generated over 10 million material candidates with Archimedean lattices. One million of those materials survived a screening for stability. Using the supercomputers in Oak Ridge National Laboratory, the researchers then took a smaller sample of 26,000 materials and ran detailed simulations to understand how the materials’ underlying atoms behaved. The researchers found magnetism in 41 percent of those structures.

From that subset, the researchers synthesized two previously undiscovered compounds, TiPdBi and TiPbSb, at Xie and Cava’s labs. Subsequent experiments showed the AI model’s predictions largely aligned with the actual material’s properties.

“We wanted to discover new materials that could have a huge potential impact by incorporating these structures that have been known to give rise to quantum properties,” says Okabe, the paper’s first author. “We already know that these materials with specific geometric patterns are interesting, so it’s natural to start with them.”

Accelerating material breakthroughs

Quantum spin liquids could unlock quantum computing by enabling stable, error-resistant qubits that serve as the basis of quantum operations. But no quantum spin liquid materials have been confirmed. Xie and Cava believe SCIGEN could accelerate the search for these materials.

“There’s a big search for quantum computer materials and topological superconductors, and these are all related to the geometric patterns of materials,” Xie says. “But experimental progress has been very, very slow,” Cava adds. “Many of these quantum spin liquid materials are subject to constraints: They have to be in a triangular lattice or a Kagome lattice. If the materials satisfy those constraints, the quantum researchers get excited; it’s a necessary but not sufficient condition. So, by generating many, many materials like that, it immediately gives experimentalists hundreds or thousands more candidates to play with to accelerate quantum computer materials research.”

“This work presents a new tool, leveraging machine learning, that can predict which materials will have specific elements in a desired geometric pattern,” says Drexel University Professor Steve May, who was not involved in the research. “This should speed up the development of previously unexplored materials for applications in next-generation electronic, magnetic, or optical technologies.”

The researchers stress that experimentation is still critical to assess whether AI-generated materials can be synthesized and how their actual properties compare with model predictions. Future work on SCIGEN could incorporate additional design rules into generative models, including chemical and functional constraints.

“People who want to change the world care about material properties more than the stability and structure of materials,” Okabe says. “With our approach, the ratio of stable materials goes down, but it opens the door to generate a whole bunch of promising materials.”

The work was supported, in part, by the U.S. Department of Energy, the National Energy Research Scientific Computing Center, the National Science Foundation, and Oak Ridge National Laboratory.

de MIT News https://ift.tt/qUAQoTK