Securing wireless communication in next-generation devices

MIT researchers have overcome a major challenge holding back the real-world deployment of microwave quantum technologies for advanced signal processing and secure communications.

The team developed a scalable platform that generates pairs of highly correlated radio frequency waves, without the need for bulky and expensive cooling equipment. In quantum technologies, these linked radio waves can be used for noise-resilient communication or high-precision radar and sensing. However, they’re usually only generated in research labs, under extremely cold conditions.

The MIT researchers fabricated a small, electronic device that can generate the same type of highly correlated signals at room temperature. 

The device incorporates a magnetic film, which interacts with microwave energy inside a metal cavity to split an incoming signal into two linked output signals. The researchers used the device to demonstrate secure communications by encoding information in a signal that could only be recovered using its partner signal.

“We’ve shown how the quantum properties of magnets can be leveraged to realize new communication and detection technologies. I hope our demonstration of this platform will enable further development of room-temperature quantum simulators, which have huge potential to enable many future discoveries,” says Qiuyuan Wang, an electrical engineering and computer science (EECS) graduate student and lead author of a paper on this technique.

Wang is joined on the paper by Aravind Karthigeyan, a graduate student at the University of Illinois at Urbana-Champaign; Chung-Tao Chou, an MIT postdoc; and senior author Luqiao Liu, an associate professor in EECS and a member of the Research Laboratory of Electronics. The research appears today in Nature Electronics.

Synchronized signals

Microwave photons are fundamental particles that form the signals used for wireless communication and sensing. 

Scientists can split one microwave photon into two tightly correlated photons using a device called a Josephson junction, which is an element of a superconducting circuit. These linked microwave photons can be used in applications like secure communications or high-performance radar systems that can detect extremely faint signals.

To enable secure communications using these correlated signals, engineers could design electronic devices that encode data in one signal by altering the signal’s properties, such that the information could only be decoded at the other end of the transmission using the matching signal. But to operate effectively, superconducting circuits must be kept at temperatures below 273 degrees Celsius, usually inside a bulky, expensive, and energy-intensive cryostat machine.

While pursuing a different line of research, the scientists in Liu’s group realized they could generate the same highly correlated microwave signals using magnets instead of cryogenically cooled superconducting circuits.

By putting a magnetic film into a microwave resonator, which is a metal cavity that traps electromagnetic energy, they could split one incoming microwave photon into a pair of perfectly synchronized signals with distinct frequencies, at room temperature.

“On its own, each signal looks random, but their phase relationship remains strongly correlated,” Wang explains.

Their device relies on magnons, which are tiny packets of magnetic energy. Typically, pumping microwave photons into a magnetic system generates a pair of correlated magnons with the same frequency. 

Even though both magnons are correlated, because they have the same frequency, scientists can’t separate them. They would need to separate the magnons to use one signal for transmission and the other for detection in secure communications.

A hybrid system

By coupling a magnetic film with a microwave resonator and carefully controlling the energy they pump into the device, the researchers could form hybrid magnon-photon waves. These hybrid waves output a pair of synchronized signals with distinct microwave frequencies.

The signals remain strongly correlated, but since the frequencies are always different and random, an attacker can’t recover the information encoded in one signal without having the matching one to use as a key.

The researchers demonstrated this by encoding a small image in the frequency of one microwave signal. They successfully decoded the signal and extracted the image using its partner.

“Magnonic systems exhibit a remarkably rich range of nonlinear dynamics, but these nonlinearities have not yet been harnessed for practical applications as extensively as those in nonlinear optics and other dynamical systems. In this work, we address one important challenge: the spectral overlap between a pair of ‘twin’ magnons generated by the same pump photon. By using the level repulsion arising from coupling between magnons and microwave photons, we were able to separate the two magnons in frequency,” says Liu. “We believe this demonstration could provide a foundation for technologies such as quantum radar, secure communications, and quantum-limited sensing, all of which rely on correlated — and ultimately entangled — microwave sources.”

This hybrid magnon-microwave system could also be used in noise-resilient communication by enabling the receiver to decode a message that has been garbled by random data that interfere with the transmission.

Correlated microwave signals are also a key element of a quantum simulator, which is a device that can emulate the complex behavior and interactions of subatomic particles that classical computers can’t handle. Scientists are developing quantum simulators to discover new drugs and materials. 

By generating correlated signals at room temperature, this new technique can improve the scalability and reduce the costs of quantum simulation. In the future, the researchers want to develop a scalable architecture for their platform, moving it one step closer to real-world deployment. They also want to explore additional applications for the process and use their platform to study the underlying physics of correlated microwave signals. 

“The creation of a non-degenerate parametric magnon-polariton platform marks an important milestone for cavity magnonics, extending the field beyond coherent microwave generation to the production of multichannel correlated microwave photons,” says Can-Ming Hu, a distinguished profess or physics and astronomy at the University of Manitoba in Canada, who was not involved with this paper. “This breakthrough will broadly impact secure microwave communications, hardware random number generation, correlation-based signal processing, and intelligent microwave sensing — all operating within the classical regime at room temperature. Looking ahead, this platform could well be remembered as the starting point for realizing quantum-inspired microwave sensing and communication technologies based on nonlinear cavity magnonics.”

This research was supported, in part, by the National Science Foundation and the U.S. Department of Energy.

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Startup brings ancient Roman concrete technology to modern construction

Concrete has served as the foundation of empires for thousands of years. Today, it’s one of the most common materials in the world. But one look at the ancient Roman concrete structures still standing suggests that ancient builders knew something about durability that we don’t.

MIT Associate Professor Admir Masic has spent his career studying ancient Roman concrete. His work has uncovered details about what gave Roman concrete its legendary durability, including the manufacturing process that endowed it with self-healing properties.

In 2021, Masic decided to apply those findings to improve the durability of modern concrete by co-founding DMAT. Today, the company has developed additional technology to create a concrete additive that increases the lifespan of concrete structures by 50 percent and reduces CO₂ emissions to 40% of traditional concrete.

The company’s concrete has been used to make complex infrastructure across Europe including underground water tanks, road barriers, and pavement in Italy and Switzerland. The company plans to expand to the U.S. soon.

“We can now offer an extremely competitively priced, self-healing product that is easy to implement and available worldwide,” Masic says. “What’s exciting to me is that this material could become the industry standard without requiring companies to change how they operate. It doesn’t introduce any uncertainty, because it’s based on ancient Roman technology that has been tested for thousands of years. By applying lessons from the past, we’re enabling a better future for the modern concrete industry.”

Applying ancient insights

Masic’s research at MIT has involved using new characterization techniques to probe the chemical makeup of ancient concrete. It has also brought him to well-preserved ancient construction sites in Pompeii, where historical practices could be deconstructed.

In a 2023 study funded, in part, by the Concrete Sustainability Hub, Masic and collaborators showed that when ancient Roman concrete cracks, reservoirs of calcium inside it desolve and recrystallize to fill in the new openings. Using that insight, the team developed new concrete formulations based on the Ancient Roman technique that deliberately retain calcium-rich lime clasts throughout the mix. The researchers spent a year testing samples to show the technique improved the mechanical performance and durability of different forms of concrete.

Those findings served as the foundation of DMAT. Masic partnered with Italian entrepreneur Paolo Sabatini to commercialize the technology shortly after the paper was published.

DMAT has since developed a large portfolio of proprietary technology on top of what was licensed from MIT. As it developed its solution, DMAT worked with company laboratories to secure safety and performance certifications in the European Union and ensure it fit modern concrete-making practices.

“At DMAT, we like to view concrete as an ecosystem,” says Sabatini, who serves as DMAT’s CEO and co-founder. “How does a material become the biggest industry in the world? There are considerations around not just materials but also transportation, price, and certifications. In order to get adoption, you need to design something that fits within the current industry’s ecosystem.”

Today DMAT supplies additives that can be mixed with concrete and mortar to extend the lifespan and performance of the materials. DMAT sells its additives to developers as well as concrete manufacturers to incorporate when mixing the concrete. More recently, the company has also introduced a line of ready-mix bagged mortars for structural restoration.

“When we work with clients, we can customize the concrete mix for their project and then supply filler using our recipe,” Sabatini explains. “We provide recipes to concrete manufacturers and engineers that improve the performance of concrete. But we also work across the supply chain with developers, architects, construction companies, and others.”

The first few years of the company were spent developing the technology and establishing relationships with the industry while attaining the necessary certifications to deploy in Europe.

“What’s good about DMAT is that the company is truly embedded into the concrete industry,” Masic says. “The company isn’t selling an idea. They have gone slow and carefully chosen projects to ensure they are successful in providing self-healing concrete without significant added cost.”

Built for scale

Other self-healing concretes use bacteria or polymer substances as additives, which can be more expensive, not to mention less familiar to people in the industry. DMAT’s founders have spent years honing their recipes to achieve self-healing properties with materials more familiar to the industry.

As a result, they believe the company is now in a strong position to scale. And scalability is crucial to make an impact in the industry: Concrete today is the most produced material in the world. It’s responsible for approximately 5-8 percent of global CO₂ emissions.

“There’s something profound about how ancient builders, without our modern chemistry, engineered self-healing material that still stands today,” Masic says. “My group research and work with DMAT is to make the modern built environment better by applying the best lessons from the past to today’s challenges.”

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Anthea Coster awarded International Union of Radio Science Appleton Prize

MIT Principal Research Scientist Anthea J. Coster was awarded the prestigious Appleton Prize at the International Union of Radio Science (URSI) General Assembly and Science Symposium in Krakow, Poland, on Aug. 16. 

The Appleton Prize recognizes career achievements and outstanding contributions to studies in ionospheric physics. Appleton awardees are regarded as pillars of the URSI atmospheric science community; the citation for Coster, an URSI Fellow, is for “pioneering research in GNSS [Global Navigation Satellite System] science, developing techniques to provide global-scale view of storm responses in the ionosphere, operationalizing novel algorithms, and providing novel ionospheric products to the community.” 

The Appleton Prize honors Sir Edward Victor Appleton, a Nobel Prize–winning physicist and former president of URSI (1934–52) who proved the existence of the ionosphere.

Coster joined MIT in 1984, originally at MIT Lincoln Laboratory, where she worked on satellite tracking applications within the Space Surveillance Complex situated at MIT Haystack Observatory. While at Lincoln, she was introduced to the Global Positioning System (GPS), the first GNSS; her GPS research at Lincoln eventually led to an appointment in Haystack’s geospace and atmospheric science research group. She continued and expanded her Lincoln-based GNSS research, focusing on ionospheric and atmospheric applications. At Haystack, Coster started as a research scientist, becoming an MIT principal research scientist in 2012; she also served as assistant director for the observatory from 2015 until 2024. 

Her career research focus spans the physics of the ionosphere, magnetosphere, and thermosphere, covering space weather and storm-time effects and coupling of these atmospheric regions, with particular expertise on GNSS positioning and measurement accuracy. Coster’s breakthrough contributions in GNSS applications to frontier geospace research span many areas, including ionosphere-magnetosphere coupling and mid-latitude ionospheric dynamics. A selected number of her accomplishments include the first real-time GNSS ionospheric monitoring system, as well as pioneering work in monitoring tropospheric water vapor with GNSS signals. She also was responsible for the first GNSS observations of storm-enhanced density, a bright and important feature that can span the heavily populated continental United States, with significant impacts to the Federal Aviation Administration Wide Area Augmentation System, which supplements traditional GPS navigation systems.

MIT Haystack Observatory director Phil Erickson says, "Dr. Coster's award from the International Radio Science Union is most well-deserved, and reflects her substantial international impact on the field of geospace remote sensing. Coster's pioneering application of GNSS signals to global and precise maps of total ionospheric electron density has produced a rich and insightful scientific output that anchors and greatly complements the multi-messenger, sensor fusion techniques at the forefront of the research field in near-Earth space weather dynamics. These areas are of critical importance to our increasingly spacefaring civilization."

Coster’s career also encompasses a lifetime of professional service contributions to the U.S. and international geophysical sciences community, including many leadership positions with the U.S. chapter of the Union of Radio Science, the Institute of Navigation, and the American Geophysical Union. She has served as co-chair of NASA's Living with a Star Program Analysis Group and is a current member of the U.S. National Academies of Science, Medicine, and Engineering Space Weather Roundtable. 

She is an author or co-author on more than 200 peer-reviewed publications, and is the principal investigator of numerous federal scientific grants from NASA, the National Science Foundation, the Office of Naval Research, and the Air Force Office of Scientific Research. Prominent results of Coster’s work are heavily used, including scientifically rich GNSS total electron content (TEC) and scintillation data products available to the research community through NSF's CEDAR Madrigal database and the Millstone Hill Geospace Facility. 

Coster has also made a number of notable contributions to science outreach, such as deploying radio instrumentation with MIT graduate students in Brazil and Peru, presenting outreach talks to high school and middle school students in Rwanda and Zambia, and installing GNSS receivers in Inuit villages and along the remote Steese Highway in Alaska. For many years, she has taught U.N.-sponsored GNSS workshops aimed at workforce education and career advancement in disadvantaged countries.

Originally from Texas, Coster attended the University of Texas at Austin as an undergraduate and earned her master's and doctorate degrees at Rice University in Houston, where she was involved with ionospheric experiments at the Arecibo Observatory in Puerto Rico. She moved to Massachusetts in 1984 to join MIT Lincoln Laboratory. 

"Anthea Coster has made seminal contributions to the state of the profession, enabling the international science community to conduct ionospheric research at spatio-temporal scales that were previously unachievable," says Larisa Goncharenko, assistant director and head of the atmospheric and geospace group at Haystack. "Her pioneering work on introducing and relating GPS measurements to fundamental research has led the community to employ GNSS as an information-rich sensor for ionospheric remote sensing and space weather monitoring. Her effort enabled countless discoveries in the near-Earth space environment that has become increasingly important for human activities in space. I am truly in awe of Anthea's pioneering accomplishments, and incredibly proud of her receiving the Appleton Prize."

With this award, MIT Haystack Observatory is now home to three URSI prize recipients. Former director and research scientist John Evans received the Appleton Prize in 1975 with a citation for "ionospheric physics, including application of the incoherent scatter technique," and research scientist Alan Rogers received the 2008 John Howard Dellinger Gold Medal for outstanding contributions to radio astronomy. 

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Q&A: Rethinking how innovation happens

Innovation is a concept that has become mythologized in the modern era: what it is, how to manage it, how to teach it, and how to get it to work for us. Despite these explorations, it remains fundamentally misunderstood, writes Eugene Fitzgerald, the Merton C. Flemings SMA Professor in MIT’s Department of Materials Science and Engineering, in his latest book, “The Invisible Engine: Why Innovation Evades Control.”

Fitzgerald draws on a decade of work leading international research programs, including the MIT and Masdar Institute Cooperative Program and the MIT-Singapore Alliance for Research and Technology, where he explored innovation as the integration of market applications, technology, and implementation.

Written at a moment when artificial intelligence is reshaping how we think about knowledge, research, and innovation, “The Invisible Engine” examines a deeper question: How does innovation actually happen, and how should society invest in it?

In this interview, Fitzgerald discusses his own experiences with innovation — including his co-invention of strained silicon at AT&T Bell Laboratories in the 1990s, which helped extend Moore’s Law, the semiconductor industry’s long-standing trend of increasing the number of transistors on chips roughly every two years — while exploring common misconceptions about innovation, how to create the conditions for it, and novel ways to prepare institutions for future uncertainty.

Q: What inspired you to write this book? 

A: The book really grew out of the last 10 years of work in research-to-market activity, from the MIT Masdar program to the MIT-Singapore Alliance. In science, we have professional journals and things like that that capture discoveries within individual fields, but these larger-scale projects — where science, economics, industry, and society all intersect — don’t really have an academic thread that connects them.

I wanted to write a book that condensed all of those connections, because the innovation process at that scale is really the intersection of many different fields. The dominant ones are science and economics, because those are the underlying principles that drive how innovation happens.

So I was interested in marking this moment in history and documenting the experiments we’ve done at scale — trying to understand how knowledge of the innovation process can be incorporated into large collaborative research programs.

What started as a practical effort to make these programs work became a broader and somewhat unexpected interest in the innovation process itself.

Q: What is the “invisible engine?” 

A: The invisible engine is this decentralized collective intelligence of different actors, which are people and companies that eventually create surprise in the marketplace, which brings great profit.

This concept of “surprise” comes from Frank Knight, an economist from the early 1900s who was trying to understand the Industrial Revolution happening around him. So he takes a close look at the entrepreneur and asks, “What does the entrepreneur do?” And his answer is that the entrepreneur takes on uncertainty. They bring something into the world without knowing exactly what will happen, and their reward is surprise — everyone is surprised that people want it and that it can be done. Because the entrepreneur is the first to discover that opportunity, they can earn a profit.

Q: How did your experience developing semiconductor technologies shape the ideas in the book? 

A: It started with Bell Labs. My colleague and I made an important discovery — we found a way of straining silicon in a thin-film form with very few defects, which had never been done before. From the physics point of view, it was a big result. But I was always interested in having impact in the world, not just scientific recognition, so I went to my manager and asked, “What do we do next?”

He said, “Go talk to the marketing people at AT&T.” In hindsight, that made perfect sense. Bell Labs, like a lot of great industrial labs, created a lot of stuff, but they couldn’t always commercialize it.

Then I came to MIT, which was an open aperture after Bell Labs. Here I could keep uncertainty open across all the elements and find convergence in different directions. Eventually I started a company, and going between institutions to stimulate things was an eye-opening experience. We eventually reached a settlement with Intel over a patent dispute because the industry discovered that strained silicon was needed to extend Moore’s Law — something we never expected.

A lot of people want things to be organized and say, “Oh yeah, look at all that chaos.” But no — the path from Bell Labs to MIT to a startup, and then to industry adoption, was the innovation process.

Q: What’s the biggest misconception about innovation? 

A: People think that all research investment works the same way if the goal is economic impact. But there are actually three different kinds of research investment, and they’re meant for different things.

There’s the one we all know about, which I call “altruistic science.” The purpose of altruistic science — in investing in an academic institution — is to produce educated people. It’s not done in the context of the world that ideas eventually have to succeed in. And if you honestly look at the direct economic yield over all these years, it’s basically zero.

Strategic research is the second investment category. As opposed to a single area of technology or science, it’s organized around a goal. A new F-35, for example, may need advancements in several fields, so the customer — in this case the government — wants them to come together. Basically, they’re taking economics out of the equation because they’re the only customer, but they have much broader uncertainty because they have multiple domains of technology that they have to deal with.

The third category is what I call “fundamental innovation.” It’s meant to represent the whole process from research to economic growth, even if it’s on 10-, 15-, or 20-year time horizons. Fundamental innovation is different because it has three variables: technology — what is physically possible; implementation — how it can be built and delivered; and market — who will adopt it, and why. Fundamental innovation involves all the necessary elements the whole time to converge on possible value. So you’re thinking about market applications the whole time, you’re thinking about new science and technology that could create new innovation options. Then you’re working in the real world saying, “OK, here’s how implementation would happen today, but maybe this could change, maybe that could change.” Not only are you doing your research, but the world is changing at the same time.

So that’s really the biggest misconception — that innovation is about an idea. It isn’t. It’s a process of working with things in the world until they become valuable.

Q: Who did you write the book for? 

A: I wrote it for multiple audiences: individual innovators and students; researchers and faculty; corporate leaders; research funders; and policy-makers. So, people who have a stake in trying to figure out, either with their careers or with their investments — whether it’s government or private — how to invest in the far future.

Q: What’s one lesson you hope readers take away?

A: For the policy people, I would say: Understand how innovation works in the economy, stop getting in its way, come up with new methods to drive it more efficiently, and realize there are three different streams of investment — altruistic, strategic, and this fundamental innovation stream that is not purposely being funded.

For students, I think understanding this is how you can actually have impact. What I point out in the book is that being involved in the innovation process makes you T-shaped: You have technical depth in one area and a broad working knowledge of many areas. If you’re doing research under these conditions, you start to learn about the world and all these different dimensions. It inherently includes business, economics, and applications. You’ve become broader, but then you still have the technical depth to drill down into any area.

For universities, this is who we should be. We should be teaching people how to do this and how to participate in these research corporations that I’m talking about. I call them third places: places that bring everybody together for this purpose — for investment, for everything else. Universities are the ones that can really trigger that, because companies aren’t going to have enough time. The government and universities should be targeting these third places for innovation, and students and faculty will be able to become more T-shaped through that interaction.

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Tackling rare genetic disorders with patient-focused science

Shannon Knight attributes her interest in neuroscience to an experience she had in high school. She and her sister attended a medical day for students at the nearby University of Illinois Chicago. As they were on their way out of the event, they walked past a room with a person holding a brain.

“We stopped and backpedaled into the room, and I was so fascinated,” says Knight. “I was able to hold the brain of a patient who had passed away of Alzheimer’s. The brain holds so much emotion, decision-making — everything. I realized that this man’s entire memory was in my hands, and something clicked for me. I decided that I really wanted to learn much more about this organ.”

Now in her sixth year of doctoral studies at MIT’s McGovern Institute for Brain Research, Knight is working on developing a novel gene therapy for childhood-onset epilepsy, specifically SYNGAP1 haploinsufficiency. This rare genetic disorder is caused by a mutation in the SYNGAP1 gene, rendering one of the two copies of the gene nonfunctional. 

SYNGAP1 is important for brain development and neuronal communication, and the disorder leads to seizures in children starting as young as 4 months old. Other symptoms include intellectual disabilities, challenges with eating and sleeping, and difficulties with movement.

While there are currently methods to address the symptoms of the disorder, such as anti-seizure medications and dietary restrictions, as the child ages, the seizures often become resistant to medications. Knight is working to develop a therapeutic using CRISPR, a biotechnology tool used to edit genes. This therapeutic aims to address the root cause of this medication resistance by focusing on the gene itself.

“The idea of leading science with empathy is something that I feel very deeply,” she says. “I hope my efforts in the lab work toward the benefit of the people affected, rather than just for the benefit of my own science.”

Researching gene therapies

Knight’s interest in the brain flourished as a neuroscience major at Bowdoin College, working with Professor Hadley Horch. While she had originally planned to be pre-med, Knight ultimately decided that it wasn’t the best fit. She enjoyed the research she did as part of her honors thesis, exploring the regeneration of neurons in the auditory system of crickets, and decided that she wanted to pursue more research in molecular neuroscience, as well as genetics.

After graduating, Knight worked at the Perrimon Lab at Harvard University, where she first learned about CRISPR, applying it in a fruit fly model. She worked for two years in the lab, co-authoring a few papers and applying to graduate schools. 

She ultimately landed in the lab of MIT Professor Guoping Feng, studying the potential of utilizing CRISPR to develop a gene therapy treatment for Phelan-McDermid Syndrome, a rare genetic disorder caused by a deletion or mutation on the 22nd chromosome.

“Many of our graduate students are passionate about making a positive impact to society through cutting-edge research, and Shannon is a perfect example,” says Feng, the James W. and Patricia T. Poitras Professor and associate director at the McGovern Institute. “She is developing gene therapy technologies that have the potential to help many kids with devastating neurodevelopmental disorders.”

Building off of the gene therapy research around Phelan-McDermid syndrome, which is now in clinical trials in patients, Knight is now in the early phases of testing gene therapy for SYNGAP1 disorder. The goal is to go through the same process for the SYNGAP1 gene therapy as for the Phelan-McDermid gene therapy — eventually obtaining U.S. Food and Drug Administration approval and beginning clinical trials. 

The testing of the gene therapy on mice with a version of SYNGAP1 disorder has alleviated seizures and all of the behavioral phenotypes. This promising work is being accelerated by the Rare Brain Disorders Nexus, an MIT initiative that launched in the fall of 2025.

“Something I think about a lot is the idea of who ‘deserves’ the attention of a gene therapy. I feel that, regardless of how rare a genetic disorder might be, it still deserves care,” says Knight. “SYNGAP1 disorder is extremely rare, only impacting one to four out of every 10,000 children. I am very fortunate to be at an institution like MIT that has so many labs and brilliant researchers working on diseases that impact large portions of society, and it was really important to me to spend my PhD years helping a small, often unseen population. Although I don’t actually have a relationship with someone who has SYNGAP1 disorder, I know so many people who feel invisible in systems, and it is really important to me to be able to focus on people who feel unseen and give them hope.”

Inspiring others in the lab

In addition to her passion for neuroscience and genetic research, Knight has also developed a love of teaching. She has been a teaching assistant for class 9.12 (Experimental Molecular Neurobiology), leading the lab portion of the course. She has enjoyed working closely with small classes of students, introducing them to the fundamentals of neuroscience lab research.

“We walked through the process of looking at a specific protein in neurons, and talked about how you can go from cell culture all the way up to a mouse brain — and all the steps in between,” she says. “It was so important to me to be able to teach the students and help them to consider all of the different types of experiments they could do.”

Knight received the Goodwin Medal in 2025 in recognition of her commitment to excellent teaching.

“I’ve talked to many of the students since then,” she says, “and many said it was one of their favorite classes.”

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Q&A: Rethinking how innovation happens

Innovation is a concept that has become mythologized in the modern era: what it is, how to manage it, how to teach it, and how to get it to work for us. Despite these explorations, it remains fundamentally misunderstood, writes Eugene Fitzgerald, the Merton C. Flemings SMA Professor in MIT’s Department of Materials Science and Engineering, in his latest book, “The Invisible Engine: Why Innovation Evades Control.”

Fitzgerald draws on a decade of work leading international research programs, including the MIT and Masdar Institute Cooperative Program and the MIT-Singapore Alliance for Research and Technology, where he explored innovation as the integration of market applications, technology, and implementation.

Written at a moment when artificial intelligence is reshaping how we think about knowledge, research, and innovation, “The Invisible Engine” examines a deeper question: How does innovation actually happen, and how should society invest in it?

In this interview, Fitzgerald discusses his own experiences with innovation — including his co-invention of strained silicon at AT&T Bell Laboratories in the 1990s, which helped extend Moore’s Law, the semiconductor industry’s long-standing trend of increasing the number of transistors on chips roughly every two years — while exploring common misconceptions about innovation, how to create the conditions for it, and novel ways to prepare institutions for future uncertainty.

Q: What inspired you to write this book? 

A: The book really grew out of the last 10 years of work in research-to-market activity, from the MIT Masdar program to the MIT-Singapore Alliance. In science, we have professional journals and things like that that capture discoveries within individual fields, but these larger-scale projects — where science, economics, industry, and society all intersect — don’t really have an academic thread that connects them.

I wanted to write a book that condensed all of those connections, because the innovation process at that scale is really the intersection of many different fields. The dominant ones are science and economics, because those are the underlying principles that drive how innovation happens.

So I was interested in marking this moment in history and documenting the experiments we’ve done at scale — trying to understand how knowledge of the innovation process can be incorporated into large collaborative research programs.

What started as a practical effort to make these programs work became a broader and somewhat unexpected interest in the innovation process itself.

Q: What is the “invisible engine?” 

A: The invisible engine is this decentralized collective intelligence of different actors, which are people and companies that eventually create surprise in the marketplace, which brings great profit.

This concept of “surprise” comes from Frank Knight, an economist from the early 1900s who was trying to understand the Industrial Revolution happening around him. So he takes a close look at the entrepreneur and asks, “What does the entrepreneur do?” And his answer is that the entrepreneur takes on uncertainty. They bring something into the world without knowing exactly what will happen, and their reward is surprise — everyone is surprised that people want it and that it can be done. Because the entrepreneur is the first to discover that opportunity, they can earn a profit.

Q: How did your experience developing semiconductor technologies shape the ideas in the book? 

A: It started with Bell Labs. My colleague and I made an important discovery — we found a way of straining silicon in a thin-film form with very few defects, which had never been done before. From the physics point of view, it was a big result. But I was always interested in having impact in the world, not just scientific recognition, so I went to my manager and asked, “What do we do next?”

He said, “Go talk to the marketing people at AT&T.” In hindsight, that made perfect sense. Bell Labs, like a lot of great industrial labs, created a lot of stuff, but they couldn’t always commercialize it.

Then I came to MIT, which was an open aperture after Bell Labs. Here I could keep uncertainty open across all the elements and find convergence in different directions. Eventually I started a company, and going between institutions to stimulate things was an eye-opening experience. We eventually reached a settlement with Intel over a patent dispute because the industry discovered that strained silicon was needed to extend Moore’s Law — something we never expected.

A lot of people want things to be organized and say, “Oh yeah, look at all that chaos.” But no — the path from Bell Labs to MIT to a startup, and then to industry adoption, was the innovation process.

Q: What’s the biggest misconception about innovation? 

A: People think that all research investment works the same way if the goal is economic impact. But there are actually three different kinds of research investment, and they’re meant for different things.

There’s the one we all know about, which I call “altruistic science.” The purpose of altruistic science — in investing in an academic institution — is to produce educated people. It’s not done in the context of the world that ideas eventually have to succeed in. And if you honestly look at the direct economic yield over all these years, it’s basically zero.

Strategic research is the second investment category. As opposed to a single area of technology or science, it’s organized around a goal. A new F-35, for example, may need advancements in several fields, so the customer — in this case the government — wants them to come together. Basically, they’re taking economics out of the equation because they’re the only customer, but they have much broader uncertainty because they have multiple domains of technology that they have to deal with.

The third category is what I call “fundamental innovation.” It’s meant to represent the whole process from research to economic growth, even if it’s on 10-, 15-, or 20-year time horizons. Fundamental innovation is different because it has three variables: technology — what is physically possible; implementation — how it can be built and delivered; and market — who will adopt it, and why. Fundamental innovation involves all the necessary elements the whole time to converge on possible value. So you’re thinking about market applications the whole time, you’re thinking about new science and technology that could create new innovation options. Then you’re working in the real world saying, “OK, here’s how implementation would happen today, but maybe this could change, maybe that could change.” Not only are you doing your research, but the world is changing at the same time.

So that’s really the biggest misconception — that innovation is about an idea. It isn’t. It’s a process of working with things in the world until they become valuable.

Q: Who did you write the book for? 

A: I wrote it for multiple audiences: individual innovators and students; researchers and faculty; corporate leaders; research funders; and policy-makers. So, people who have a stake in trying to figure out, either with their careers or with their investments — whether it’s government or private — how to invest in the far future.

Q: What’s one lesson you hope readers take away?

A: For the policy people, I would say: Understand how innovation works in the economy, stop getting in its way, come up with new methods to drive it more efficiently, and realize there are three different streams of investment — altruistic, strategic, and this fundamental innovation stream that is not purposely being funded.

For students, I think understanding this is how you can actually have impact. What I point out in the book is that being involved in the innovation process makes you T-shaped: You have technical depth in one area and a broad working knowledge of many areas. If you’re doing research under these conditions, you start to learn about the world and all these different dimensions. It inherently includes business, economics, and applications. You’ve become broader, but then you still have the technical depth to drill down into any area.

For universities, this is who we should be. We should be teaching people how to do this and how to participate in these research corporations that I’m talking about. I call them third places: places that bring everybody together for this purpose — for investment, for everything else. Universities are the ones that can really trigger that, because companies aren’t going to have enough time. The government and universities should be targeting these third places for innovation, and students and faculty will be able to become more T-shaped through that interaction.

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Mathematical framework connects biological principles to manufacturable, adaptive materials

The scales of a pine cone open in low humidity to scatter seeds, but close in damp conditions to protect seeds from moisture. An artificial material with the same behavior could be useful in applications like moisture-responsive shingles for passive cooling.

MIT researchers have now developed a system that simplifies the process of designing this type of bioinspired material. 

Their framework captures how mechanisms across length scales in a natural system, like the cells, fibers, and tissues inside a pine cone, work together to achieve unique properties. It then formally translates that behavior in an engineered system. 

The framework organizes biological behavior into building blocks that can be used to design synthetic structures that can be mathematically validated to perform the same way, and fabricated using a 3D printer. 

By taking much of the guesswork out of this design process, the framework could help engineers more readily create new adaptive materials while cutting development time and eliminating costs from failed prototypes. This framework could one day be used to design soft robotic grippers that respond automatically to their environment without any complex electronics, or morphing structures for airplane wings that predictably change their shape in response to temperature shifts.

“I’ve always been fascinated with natural materials and how complex behavior emerges from very simple building blocks,” says Lee Marom, an MIT graduate student in the departments of Mechanical Engineering and Architecture and lead author of a paper on this framework. “What really excites me about this work is going beyond bio-inspiration to what we could call ‘bio-derivation,’ where we move past observing a unique behavior to capturing the relationships and mechanisms that are actually producing that behavior, and then finding a systematic way to translate them into an engineered system.”

Marom is joined on the paper by corresponding author Markus Buehler, the Jerry McAfee Professor of Engineering in the departments of Civil and Environmental Engineering and Mechanical Engineering; Gioele Zardini, the Rudge and Nancy Allen Assistant Professor of Civil and Environmental Engineering, a principal investigator in the Laboratory for Information and Decision Systems, and an affiliate faculty with the Institute for Data, Systems, and Society; and Skylar Tibbits, an associate professor in the Department of Architecture. The research appears in the Journal of the Mechanics and Physics of Solids.

Biological building blocks

Pine cones can open and close their scales in response to humidity because of complex interactions within the organism’s structure. 

Shifts in humidity cause changes in microscopic cellulose fibers, which then cause transformations in larger groupings of fibers called laminas, which impact tissue layers, and so on, all the way up to the pinecone we see hanging from a tree branch.

“We instantiated the framework on the pine cone because it gives us a relatively simple, well-understood mechanism to demonstrate how the framework works. But its value becomes even greater as we apply it to more complex systems,” Marom says.

For engineers, the challenge is not necessarily reproducing an individual behavior, but translating the mechanisms and relationships that produce it across length scales. Without an explicit framework, these relationships need to be reformulated for each new system. 

To streamline the material design process, MIT researchers created a mathematical framework that captures how the components at each scale in a natural object work together to exhibit a certain behavior. The framework carries the design all the way to fabrication, translating the engineered behavior into verified manufacturing specifications and executable code that is used to 3D-print the object.

“What we were missing was a way to connect the mathematical description of a natural system all the way to its physical realization. The goal of this framework is to make that entire chain explicit so we can reason about what has to be preserved at each step,” Marom says.

The framework utilizes tools from category theory, which is a systematic method to compose larger systems from smaller ones in a way that is guaranteed to succeed.

Using category theory, the system maps out how a stimulus, such as humidity, causes a response at each level of the biological hierarchy within an organism like a pine cone. It models each level of the biological hierarchy as a separate building block that is independently validated.

Then the framework constructs a larger system from these building blocks by employing mathematical rules to ensure there is a valid transition between each step in the hierarchy. 

It assigns each building block in the natural system to a synthetic counterpart. In this way, the engineered material preserves the stimulus-response interactions that cause the natural organism’s unique behavior.

The work extends a research program in Buehler’s laboratory spanning more than a decade. 

Earlier studies used category theory to describe hierarchical materials and determine when building blocks could be replaced while preserving higher-level function. In subsequent work, Buehler and colleagues introduced “categorical prototyping,” using the same mathematics to preserve selected molecular-scale mechanics when translating computational models into large-scale 3D-printed prototypes. 

The new framework takes the next step by closing the entire chain, from multiscale biological mechanics, through an engineered realization and fabrication specification, to an experimentally validated, machine-executable design.

“Biological materials derive their extraordinary functionality from relationships that span scales, from molecular and fiber-level mechanisms to whole structures. Category theory gives us a way to make those relationships explicit and transferable. Once that design logic is captured mathematically, nature becomes a library of composable mechanisms that can be translated, recombined, and realized in new material systems,” Buehler says.

Compositional structure 

“Once we know that the relationships we mapped are valid, we can start recombining them in new ways. That means the framework isn’t only describing existing systems, it can also help us reason about ones we haven’t built before,” Marom explains.

For instance, the engineers mapped the humidity-driven bending behavior in a pine cone and the humidity-driven twisting behavior of a wheat awn as separate sets of building blocks. 

Then they combined some building blocks from each to design and fabricate a new type of actuator that exhibits thermal twisting behavior, without the need to do any new design work. When tested, the twisting actuator performed as the researchers expected.

In the future, engineers could use this framework to reliably combine verified components into new, bio-inspired designs for adaptive materials in applications like robotics, biomedical devices, or wearable technology.

“The systematization of our framework allows you to reuse pieces without needing to start from scratch each time, saving a huge amount of computation. That’s the real-world payoff,” Zardini says.

Now that the researchers have laid the groundwork with this mathematical framework, they can apply it to objects with more complex mechanics. They also plan to incorporate artificial intelligence models into their pipeline to expedite the discovery of new adaptive materials. 

“We have shown that the boundaries between disciplines do not matter as much as we think they do. Some of the principles from category theory can be used to guide and empower materials design. These mathematical structures seem to really have no boundaries,” Zardini says.

“The larger vision is physical AI: intelligence that can reason in terms of physical mechanisms and then turn those ideas into matter. Here we are beginning to build the infrastructure for that — composable physical knowledge, mathematical rules for determining what can be combined, and a path from a new design concept all the way to machine instructions and fabrication. Ultimately, this could allow AI not only to discover new materials and mechanisms, but to physically realize and test what it discovers,” Buehler says.  

This research was supported, in part, by the MIT Lemelson Engineering Fellowship, Singapore DSO National Laboratories, and the MIT Generative AI Impact Consortium.

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