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    Fighting for the health of the planet with AI

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    For Priya Donti, childhood trips to India were more than an opportunity to visit extended family. The biennial journeys activated in her a motivation that continues to shape her research and her teaching.Contrasting her family home in Massachusetts, Donti — now the Silverman Family Career Development Professor in the Department of Electrical Engineering and Computer Science (EECS), a shared position between the MIT Schwarzman College of Computing and EECS, and a principal investigator at the MIT Laboratory for Information and Decision Systems (LIDS) — was struck by the disparities in how people live.“It was very clear to me the extent to which inequity is a rampant issue around the world,” Donti says. “From a young age, I knew that I definitely wanted to address that issue.”That motivation was further stoked by a high school biology teacher, who focused his class on climate and sustainability.“We learned that climate change, this huge, important issue, would exacerbate inequity,” Donti says. “That really stuck with me and put a fire in my belly.”So, when Donti enrolled at Harvey Mudd College, she thought she would direct her energy toward the study of chemistry or materials science to create next-generation solar panels.Those plans, however, were jilted. Donti “fell in love” with computer science, and then discovered work by researchers in the United Kingdom who were arguing that artificial intelligence and machine learning would be essential to help integrate renewables into power grids.“It was the first time I’d seen those two interests brought together,” she says. “I got hooked and have been working on that topic ever since.”Pursuing a PhD at Carnegie Mellon University, Donti was able to design her degree to include computer science and public policy. In her research, she explored the need for fundamental algorithms and tools that could manage, at scale, power grids relying heavily on renewables.“I wanted to have a hand in developing those algorithms and tool kits by creating new machine learning techniques grounded in computer science,” she says. “But I wanted to make sure that the way I was doing the work was grounded both in the actual energy systems domain and working with people in that domain” to provide what was actually needed.While Donti was working on her PhD, she co-founded a nonprofit called Climate Change AI. Her objective, she says, was to help the community of people involved in climate and sustainability — “be they computer scientists, academics, practitioners, or policymakers” — to come together and access resources, connection, and education “to help them along that journey.”“In the climate space,” she says, “you need experts in particular climate change-related sectors, experts in different technical and social science tool kits, problem owners, affected users, policymakers who know the regulations — all of those — to have on-the-ground scalable impact.”When Donti came to MIT in September 2023, it was not surprising that she was drawn by its initiatives directing the application of computer science toward society’s biggest problems, especially the current threat to the health of the planet.“We’re really thinking about where technology has a much longer-horizon impact and how technology, society, and policy all have to work together,” Donti says. “Technology is not just one-and-done and monetizable in the context of a year.”Her work uses deep learning models to incorporate the physics and hard constraints of electric power systems that employ renewables for better forecasting, optimization, and control.“Machine learning is already really widely used for things like solar power forecasting, which is a prerequisite to managing and balancing power grids,” she says. “My focus is, how do you improve the algorithms for actually balancing power grids in the face of a range of time-varying renewables?”Among Donti’s breakthroughs is a promising solution for power grid operators to be able to optimize for cost, taking into account the actual physical realities of the grid, rather than relying on approximations. While the solution is not yet deployed, it appears to work 10 times faster, and far more cheaply, than previous technologies, and has attracted the attention of grid operators.Another technology she is developing works to provide data that can be used in training machine learning systems for power system optimization. In general, much data related to the systems is private, either because it is proprietary or because of security concerns. Donti and her research group are working to create synthetic data and benchmarks that, Donti says, “can help to expose some of the underlying problems” in making power systems more efficient.“The question is,” Donti says, “can we bring our datasets to a point such that they are just hard enough to drive progress?”For her efforts, Donti has been awarded the U.S. Department of Energy Computational Science Graduate Fellowship and the NSF Graduate Research Fellowship. She was recognized as part of MIT Technology Review’s 2021 list of “35 Innovators Under 35” and Vox’s 2023 “Future Perfect 50.”Next spring, Donti will co-teach a class called AI for Climate Action with Sara Beery, EECS assistant professor, whose focus is AI for biodiversity and ecosystems, and Abigail Bodner, assistant professor in the departments of EECS and Earth, Atmospheric and Planetary Sciences, whose focus is AI for climate and Earth science.“We’re all super-excited about it,” Donti says.Coming to MIT, Donti says, “I knew that there would be an ecosystem of people who really cared, not just about success metrics like publications and citation counts, but about the impact of our work on society.” More

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      New prediction model could improve the reliability of fusion power plants

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      Tokamaks are machines that are meant to hold and harness the power of the sun. These fusion machines use powerful magnets to contain a plasma hotter than the sun’s core and push the plasma’s atoms to fuse and release energy. If tokamaks can operate safely and efficiently, the machines could one day provide clean and limitless fusion energy.Today, there are a number of experimental tokamaks in operation around the world, with more underway. Most are small-scale research machines built to investigate how the devices can spin up plasma and harness its energy. One of the challenges that tokamaks face is how to safely and reliably turn off a plasma current that is circulating at speeds of up to 100 kilometers per second, at temperatures of over 100 million degrees Celsius.Such “rampdowns” are necessary when a plasma becomes unstable. To prevent the plasma from further disrupting and potentially damaging the device’s interior, operators ramp down the plasma current. But occasionally the rampdown itself can destabilize the plasma. In some machines, rampdowns have caused scrapes and scarring to the tokamak’s interior — minor damage that still requires considerable time and resources to repair.Now, scientists at MIT have developed a method to predict how plasma in a tokamak will behave during a rampdown. The team combined machine-learning tools with a physics-based model of plasma dynamics to simulate a plasma’s behavior and any instabilities that may arise as the plasma is ramped down and turned off. The researchers trained and tested the new model on plasma data from an experimental tokamak in Switzerland. They found the method quickly learned how plasma would evolve as it was tuned down in different ways. What’s more, the method achieved a high level of accuracy using a relatively small amount of data. This training efficiency is promising, given that each experimental run of a tokamak is expensive and quality data is limited as a result.The new model, which the team highlights this week in an open-access Nature Communications paper, could improve the safety and reliability of future fusion power plants.“For fusion to be a useful energy source it’s going to have to be reliable,” says lead author Allen Wang, a graduate student in aeronautics and astronautics and a member of the Disruption Group at MIT’s Plasma Science and Fusion Center (PSFC). “To be reliable, we need to get good at managing our plasmas.”The study’s MIT co-authors include PSFC Principal Research Scientist and Disruptions Group leader Cristina Rea, and members of the Laboratory for Information and Decision Systems (LIDS) Oswin So, Charles Dawson, and Professor Chuchu Fan, along with Mark (Dan) Boyer of Commonwealth Fusion Systems and collaborators from the Swiss Plasma Center in Switzerland.“A delicate balance”Tokamaks are experimental fusion devices that were first built in the Soviet Union in the 1950s. The device gets its name from a Russian acronym that translates to a “toroidal chamber with magnetic coils.” Just as its name describes, a tokamak is toroidal, or donut-shaped, and uses powerful magnets to contain and spin up a gas to temperatures and energies high enough that atoms in the resulting plasma can fuse and release energy.Today, tokamak experiments are relatively low-energy in scale, with few approaching the size and output needed to generate safe, reliable, usable energy. Disruptions in experimental, low-energy tokamaks are generally not an issue. But as fusion machines scale up to grid-scale dimensions, controlling much higher-energy plasmas at all phases will be paramount to maintaining a machine’s safe and efficient operation.“Uncontrolled plasma terminations, even during rampdown, can generate intense heat fluxes damaging the internal walls,” Wang notes. “Quite often, especially with the high-performance plasmas, rampdowns actually can push the plasma closer to some instability limits. So, it’s a delicate balance. And there’s a lot of focus now on how to manage instabilities so that we can routinely and reliably take these plasmas and safely power them down. And there are relatively few studies done on how to do that well.”Bringing down the pulseWang and his colleagues developed a model to predict how a plasma will behave during tokamak rampdown. While they could have simply applied machine-learning tools such as a neural network to learn signs of instabilities in plasma data, “you would need an ungodly amount of data” for such tools to discern the very subtle and ephemeral changes in extremely high-temperature, high-energy plasmas, Wang says.Instead, the researchers paired a neural network with an existing model that simulates plasma dynamics according to the fundamental rules of physics. With this combination of machine learning and a physics-based plasma simulation, the team found that only a couple hundred pulses at low performance, and a small handful of pulses at high performance, were sufficient to train and validate the new model.The data they used for the new study came from the TCV, the Swiss “variable configuration tokamak” operated by the Swiss Plasma Center at EPFL (the Swiss Federal Institute of Technology Lausanne). The TCV is a small experimental fusion experimental device that is used for research purposes, often as test bed for next-generation device solutions. Wang used the data from several hundred TCV plasma pulses that included properties of the plasma such as its temperature and energies during each pulse’s ramp-up, run, and ramp-down. He trained the new model on this data, then tested it and found it was able to accurately predict the plasma’s evolution given the initial conditions of a particular tokamak run.The researchers also developed an algorithm to translate the model’s predictions into practical “trajectories,” or plasma-managing instructions that a tokamak controller can automatically carry out to for instance adjust the magnets or temperature maintain the plasma’s stability. They implemented the algorithm on several TCV runs and found that it produced trajectories that safely ramped down a plasma pulse, in some cases faster and without disruptions compared to runs without the new method.“At some point the plasma will always go away, but we call it a disruption when the plasma goes away at high energy. Here, we ramped the energy down to nothing,” Wang notes. “We did it a number of times. And we did things much better across the board. So, we had statistical confidence that we made things better.”The work was supported in part by Commonwealth Fusion Systems (CFS), an MIT spinout that intends to build the world’s first compact, grid-scale fusion power plant. The company is developing a demo tokamak, SPARC, designed to produce net-energy plasma, meaning that it should generate more energy than it takes to heat up the plasma. Wang and his colleagues are working with CFS on ways that the new prediction model and tools like it can better predict plasma behavior and prevent costly disruptions to enable safe and reliable fusion power.“We’re trying to tackle the science questions to make fusion routinely useful,” Wang says. “What we’ve done here is the start of what is still a long journey. But I think we’ve made some nice progress.”Additional support for the research came from the framework of the EUROfusion Consortium, via the Euratom Research and Training Program and funded by the Swiss State Secretariat for Education, Research, and Innovation. More

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      Palladium filters could enable cheaper, more efficient generation of hydrogen fuel

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      Palladium is one of the keys to jump-starting a hydrogen-based energy economy. The silvery metal is a natural gatekeeper against every gas except hydrogen, which it readily lets through. For its exceptional selectivity, palladium is considered one of the most effective materials at filtering gas mixtures to produce pure hydrogen.Today, palladium-based membranes are used at commercial scale to provide pure hydrogen for semiconductor manufacturing, food processing, and fertilizer production, among other applications in which the membranes operate at modest temperatures. If palladium membranes get much hotter than around 800 kelvins, they can break down.Now, MIT engineers have developed a new palladium membrane that remains resilient at much higher temperatures. Rather than being made as a continuous film, as most membranes are, the new design is made from palladium that is deposited as “plugs” into the pores of an underlying supporting material. At high temperatures, the snug-fitting plugs remain stable and continue separating out hydrogen, rather than degrading as a surface film would.The thermally stable design opens opportunities for membranes to be used in hydrogen-fuel-generating technologies such as compact steam methane reforming and ammonia cracking — technologies that are designed to operate at much higher temperatures to produce hydrogen for zero-carbon-emitting fuel and electricity.“With further work on scaling and validating performance under realistic industrial feeds, the design could represent a promising route toward practical membranes for high-temperature hydrogen production,” says Lohyun Kim PhD ’24, a former graduate student in MIT’s Department of Mechanical Engineering.Kim and his colleagues report details of the new membrane in a study appearing today in the journal Advanced Functional Materials. The study’s co-authors are Randall Field, director of research at the MIT Energy Initiative (MITEI); former MIT chemical engineering graduate student Chun Man Chow PhD ’23; Rohit Karnik, the Jameel Professor in the Department of Mechanical Engineering at MIT and the director of the Abdul Latif Jameel Water and Food Systems Lab (J-WAFS); and Aaron Persad, a former MIT research scientist in mechanical engineering who is now an assistant professor at the University of Maryland Eastern Shore.Compact futureThe team’s new design came out of a MITEI project related to fusion energy. Future fusion power plants, such as the one MIT spinout Commonwealth Fusion Systems is designing, will involve circulating hydrogen isotopes of deuterium and tritium at extremely high temperatures to produce energy from the isotopes’ fusing. The reactions inevitably produce other gases that will have to be separated, and the hydrogen isotopes will be recirculated into the main reactor for further fusion.Similar issues arise in a number of other processes for producing hydrogen, where gases must be separated and recirculated back into a reactor. Concepts for such recirculating systems would require first cooling down the gas before it can pass through hydrogen-separating membranes — an expensive and energy-intensive step that would involve additional machinery and hardware.“One of the questions we were thinking about is: Can we develop membranes which could be as close to the reactor as possible, and operate at higher temperatures, so we don’t have to pull out the gas and cool it down first?” Karnik says. “It would enable more energy-efficient, and therefore cheaper and compact, fusion systems.”The researchers looked for ways to improve the temperature resistance of palladium membranes. Palladium is the most effective metal used today to separate hydrogen from a variety of gas mixtures. It naturally attracts hydrogen molecules (H2) to its surface, where the metal’s electrons interact with and weaken the molecule’s bonds, causing H2 to temporarily break apart into its respective atoms. The individual atoms then diffuse through the metal and join back up on the other side as pure hydrogen.Palladium is highly effective at permeating hydrogen, and only hydrogen, from streams of various gases. But conventional membranes typically can operate at temperatures of up to 800 kelvins before the film starts to form holes or clumps up into droplets, allowing other gases to flow through.Plugging inKarnik, Kim and their colleagues took a different design approach. They observed that at high temperatures, palladium will start to shrink up. In engineering terms, the material is acting to reduce surface energy. To do this, palladium, and most other materials and even water, will pull apart and form droplets with the smallest surface energy. The lower the surface energy, the more stable the material can be against further heating.This gave the team an idea: If a supporting material’s pores could be “plugged” with deposits of palladium — essentially already forming a droplet with the lowest surface energy — the tight quarters might substantially increase palladium’s heat tolerance while preserving the membrane’s selectivity for hydrogen.To test this idea, they fabricated small chip-sized samples of membrane using a porous silica supporting layer (each pore measuring about half a micron wide), onto which they deposited a very thin layer of palladium. They applied techniques to essentially grow the palladium into the pores, and polished down the surface to remove the palladium layer and leave palladium only inside the pores.They then placed samples in a custom-built apparatus in which they flowed hydrogen-containing gas of various mixtures and temperatures to test its separation performance. The membranes remained stable and continued to separate hydrogen from other gases even after experiencing temperatures of up to 1,000 kelvins for over 100 hours — a significant improvement over conventional film-based membranes.“The use of palladium film membranes are generally limited to below around 800 kelvins, at which point they degrade,” Kim says. “Our plug design therefore extends palladium’s effective heat resilience by roughly at least 200 kelvins and maintains integrity far longer under extreme conditions.”These conditions are within the range of hydrogen-generating technologies such as steam methane reforming and ammonia cracking.Steam methane reforming is an established process that has required complex, energy-intensive systems to preprocess methane to a form where pure hydrogen can be extracted. Such preprocessing steps could be replaced with a compact “membrane reactor,” through which a methane gas would directly flow, and the membrane inside would filter out pure hydrogen. Such reactors would significantly cut down the size, complexity, and cost of producing hydrogen from steam methane reforming, and Kim estimates a membrane would have to work reliably in temperatures of up to nearly 1,000 kelvins. The team’s new membrane could work well within such conditions.Ammonia cracking is another way to produce hydrogen, by “cracking” or breaking apart ammonia. As ammonia is very stable in liquid form, scientists envision that it could be used as a carrier for hydrogen and be safely transported to a hydrogen fuel station, where ammonia could be fed into a membrane reactor that again pulls out hydrogen and pumps it directly into a fuel cell vehicle. Ammonia cracking is still largely in pilot and demonstration stages, and Kim says any membrane in an ammonia cracking reactor would likely operate at temperatures of around 800 kelvins — within the range of the group’s new plug-based design.Karnik emphasizes that their results are just a start. Adopting the membrane into working reactors will require further development and testing to ensure it remains reliable over much longer periods of time.“We showed that instead of making a film, if you make discretized nanostructures you can get much more thermally stable membranes,” Karnik says. “It provides a pathway for designing membranes for extreme temperatures, with the added possibility of using smaller amounts of expensive palladium, toward making hydrogen production more efficient and affordable. There is potential there.”This work was supported by Eni S.p.A. via the MIT Energy Initiative. More

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

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      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 impactMuch 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 progressEvery 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. More

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      New method could monitor corrosion and cracking in a nuclear reactor

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      MIT researchers have developed a technique that enables real-time, 3D monitoring of corrosion, cracking, and other material failure processes inside a nuclear reactor environment.This could allow engineers and scientists to design safer nuclear reactors that also deliver higher performance for applications like electricity generation and naval vessel propulsion.During their experiments, the researchers utilized extremely powerful X-rays to mimic the behavior of neutrons interacting with a material inside a nuclear reactor.They found that adding a buffer layer of silicon dioxide between the material and its substrate, and keeping the material under the X-ray beam for a longer period of time, improves the stability of the sample. This allows for real-time monitoring of material failure processes.By reconstructing 3D image data on the structure of a material as it fails, researchers could design more resilient materials that can better withstand the stress caused by irradiation inside a nuclear reactor.“If we can improve materials for a nuclear reactor, it means we can extend the life of that reactor. It also means the materials will take longer to fail, so we can get more use out of a nuclear reactor than we do now. The technique we’ve demonstrated here allows to push the boundary in understanding how materials fail in real-time,” says Ericmoore Jossou, who has shared appointments in the Department of Nuclear Science and Engineering (NSE), where he is the John Clark Hardwick Professor, and the Department of Electrical Engineering and Computer Science (EECS), and the MIT Schwarzman College of Computing.Jossou, senior author of a study on this technique, is joined on the paper by lead author David Simonne, an NSE postdoc; Riley Hultquist, a graduate student in NSE; Jiangtao Zhao, of the European Synchrotron; and Andrea Resta, of Synchrotron SOLEIL. The research was published Tuesday by the journal Scripta Materiala.“Only with this technique can we measure strain with a nanoscale resolution during corrosion processes. Our goal is to bring such novel ideas to the nuclear science community while using synchrotrons both as an X-ray probe and radiation source,” adds Simonne.Real-time imagingStudying real-time failure of materials used in advanced nuclear reactors has long been a goal of Jossou’s research group.Usually, researchers can only learn about such material failures after the fact, by removing the material from its environment and imaging it with a high-resolution instrument.“We are interested in watching the process as it happens. If we can do that, we can follow the material from beginning to end and see when and how it fails. That helps us understand a material much better,” he says.They simulate the process by firing an extremely focused X-ray beam at a sample to mimic the environment inside a nuclear reactor. The researchers must use a special type of high-intensity X-ray, which is only found in a handful of experimental facilities worldwide.For these experiments they studied nickel, a material incorporated into alloys that are commonly used in advanced nuclear reactors. But before they could start the X-ray equipment, they had to prepare a sample.To do this, the researchers used a process called solid state dewetting, which involves putting a thin film of the material onto a substrate and heating it to an extremely high temperature in a furnace until it transforms into single crystals.“We thought making the samples was going to be a walk in the park, but it wasn’t,” Jossou says.As the nickel heated up, it interacted with the silicon substrate and formed a new chemical compound, essentially derailing the entire experiment. After much trial-and-error, the researchers found that adding a thin layer of silicon dioxide between the nickel and substrate prevented this reaction.But when crystals formed on top of the buffer layer, they were highly strained. This means the individual atoms had moved slightly to new positions, causing distortions in the crystal structure.Phase retrieval algorithms can typically recover the 3D size and shape of a crystal in real-time, but if there is too much strain in the material, the algorithms will fail.However, the team was surprised to find that keeping the X-ray beam trained on the sample for a longer period of time caused the strain to slowly relax, due to the silicon buffer layer. After a few extra minutes of X-rays, the sample was stable enough that they could utilize phase retrieval algorithms to accurately recover the 3D shape and size of the crystal.“No one had been able to do that before. Now that we can make this crystal, we can image electrochemical processes like corrosion in real time, watching the crystal fail in 3D under conditions that are very similar to inside a nuclear reactor. This has far-reaching impacts,” he says.They experimented with a different substrate, such as niobium doped strontium titanate, and found that only a silicon dioxide buffered silicon wafer created this unique effect.An unexpected resultAs they fine-tuned the experiment, the researchers discovered something else.They could also use the X-ray beam to precisely control the amount of strain in the material, which could have implications for the development of microelectronics.In the microelectronics community, engineers often introduce strain to deform a material’s crystal structure in a way that boosts its electrical or optical properties.“With our technique, engineers can use X-rays to tune the strain in microelectronics while they are manufacturing them. While this was not our goal with these experiments, it is like getting two results for the price of one,” he adds.In the future, the researchers want to apply this technique to more complex materials like steel and other metal alloys used in nuclear reactors and aerospace applications. They also want to see how changing the thickness of the silicon dioxide buffer layer impacts their ability to control the strain in a crystal sample.“This discovery is significant for two reasons. First, it provides fundamental insight into how nanoscale materials respond to radiation — a question of growing importance for energy technologies, microelectronics, and quantum materials. Second, it highlights the critical role of the substrate in strain relaxation, showing that the supporting surface can determine whether particles retain or release strain when exposed to focused X-ray beams,” says Edwin Fohtung, an associate professor at the Rensselaer Polytechnic Institute, who was not involved with this work.This work was funded, in part, by the MIT Faculty Startup Fund and the U.S. Department of Energy. The sample preparation was carried out, in part, at the MIT.nano facilities. More

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      Jessika Trancik named director of the Sociotechnical Systems Research Center

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      Jessika Trancik, a professor in MIT’s Institute for Data, Systems, and Society, has been named the new director of the Sociotechnical Systems Research Center (SSRC), effective July 1. The SSRC convenes and supports researchers focused on problems and solutions at the intersection of technology and its societal impacts.Trancik conducts research on technology innovation and energy systems. At the Trancik Lab, she and her team develop methods drawing on engineering knowledge, data science, and policy analysis. Their work examines the pace and drivers of technological change, helping identify where innovation is occurring most rapidly, how emerging technologies stack up against existing systems, and which performance thresholds matter most for real-world impact. Her models have been used to inform government innovation policy and have been applied across a wide range of industries.“Professor Trancik’s deep expertise in the societal implications of technology, and her commitment to developing impactful solutions across industries, make her an excellent fit to lead SSRC,” says Maria C. Yang, interim dean of engineering and William E. Leonhard (1940) Professor of Mechanical Engineering.Much of Trancik’s research focuses on the domain of energy systems, and establishing methods for energy technology evaluation, including of their costs, performance, and environmental impacts. She covers a wide range of energy services — including electricity, transportation, heating, and industrial processes. Her research has applications in solar and wind energy, energy storage, low-carbon fuels, electric vehicles, and nuclear fission. Trancik is also known for her research on extreme events in renewable energy availability.A prolific researcher, Trancik has helped measure progress and inform the development of solar photovoltaics, batteries, electric vehicle charging infrastructure, and other low-carbon technologies — and anticipate future trends. One of her widely cited contributions includes quantifying learning rates and identifying where targeted investments can most effectively accelerate innovation. These tools have been used by U.S. federal agencies, international organizations, and the private sector to shape energy R&D portfolios, climate policy, and infrastructure planning.Trancik is committed to engaging and informing the public on energy consumption. She and her team developed the app carboncounter.com, which helps users choose cars with low costs and low environmental impacts.As an educator, Trancik teaches courses for students across MIT’s five schools and the MIT Schwarzman College of Computing.“The question guiding my teaching and research is how do we solve big societal challenges with technology, and how can we be more deliberate in developing and supporting technologies to get us there?” Trancik said in an article about course IDS.521/IDS.065 (Energy Systems for Climate Change Mitigation).Trancik received her undergraduate degree in materials science and engineering from Cornell University. As a Rhodes Scholar, she completed her PhD in materials science at the University of Oxford. She subsequently worked for the United Nations in Geneva, Switzerland, and the Earth Institute at Columbia University. After serving as an Omidyar Research Fellow at the Santa Fe Institute, she joined MIT in 2010 as a faculty member.Trancik succeeds Fotini Christia, the Ford International Professor of Social Sciences in the Department of Political Science and director of IDSS, who previously served as director of SSRC. More

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      Surprisingly diverse innovations led to dramatically cheaper solar panels

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      The cost of solar panels has dropped by more than 99 percent since the 1970s, enabling widespread adoption of photovoltaic systems that convert sunlight into electricity.A new MIT study drills down on specific innovations that enabled such dramatic cost reductions, revealing that technical advances across a web of diverse research efforts and industries played a pivotal role.The findings could help renewable energy companies make more effective R&D investment decisions and aid policymakers in identifying areas to prioritize to spur growth in manufacturing and deployment.The researchers’ modeling approach shows that key innovations often originated outside the solar sector, including advances in semiconductor fabrication, metallurgy, glass manufacturing, oil and gas drilling, construction processes, and even legal domains.“Our results show just how intricate the process of cost improvement is, and how much scientific and engineering advances, often at a very basic level, are at the heart of these cost reductions. A lot of knowledge was drawn from different domains and industries, and this network of knowledge is what makes these technologies improve,” says study senior author Jessika Trancik, a professor in MIT’s Institute for Data, Systems, and Society.Trancik is joined on the paper by co-lead authors Goksin Kavlak, a former IDSS graduate student and postdoc who is now a senior energy associate at the Brattle Group; Magdalena Klemun, a former IDSS graduate student and postdoc who is now an assistant professor at Johns Hopkins University; former MIT postdoc Ajinkya Kamat; as well as Brittany Smith and Robert Margolis of the National Renewable Energy Laboratory. The research appears today in PLOS ONE.Identifying innovationsThis work builds on mathematical models that the researchers previously developed that tease out the effects of engineering technologies on the cost of photovoltaic (PV) modules and systems.In this study, the researchers aimed to dig even deeper into the scientific advances that drove those cost declines.They combined their quantitative cost model with a detailed, qualitative analysis of innovations that affected the costs of PV system materials, manufacturing steps, and deployment processes.“Our quantitative cost model guided the qualitative analysis, allowing us to look closely at innovations in areas that are hard to measure due to a lack of quantitative data,” Kavlak says.Building on earlier work identifying key cost drivers — such as the number of solar cells per module, wiring efficiency, and silicon wafer area — the researchers conducted a structured scan of the literature for innovations likely to affect these drivers. Next, they grouped these innovations to identify patterns, revealing clusters that reduced costs by improving materials or prefabricating components to streamline manufacturing and installation. Finally, the team tracked industry origins and timing for each innovation, and consulted domain experts to zero in on the most significant innovations.All told, they identified 81 unique innovations that affected PV system costs since 1970, from improvements in antireflective coated glass to the implementation of fully online permitting interfaces.“With innovations, you can always go to a deeper level, down to things like raw materials processing techniques, so it was challenging to know when to stop. Having that quantitative model to ground our qualitative analysis really helped,” Trancik says.They chose to separate PV module costs from so-called balance-of-system (BOS) costs, which cover things like mounting systems, inverters, and wiring.PV modules, which are wired together to form solar panels, are mass-produced and can be exported, while many BOS components are designed, built, and sold at the local level.“By examining innovations both at the BOS level and within the modules, we identify the different types of innovations that have emerged in these two parts of PV technology,” Kavlak says.BOS costs depend more on soft technologies, nonphysical elements such as permitting procedures, which have contributed significantly less to PV’s past cost improvement compared to hardware innovations.“Often, it comes down to delays. Time is money, and if you have delays on construction sites and unpredictable processes, that affects these balance-of-system costs,” Trancik says.Innovations such as automated permitting software, which flags code-compliant systems for fast-track approval, show promise. Though not yet quantified in this study, the team’s framework could support future analysis of their economic impact and similar innovations that streamline deployment processes.Interconnected industriesThe researchers found that innovations from the semiconductor, electronics, metallurgy, and petroleum industries played a major role in reducing both PV and BOS costs, but BOS costs were also impacted by innovations in software engineering and electric utilities.Noninnovation factors, like efficiency gains from bulk purchasing and the accumulation of knowledge in the solar power industry, also reduced some cost variables.In addition, while most PV panel innovations originated in research organizations or industry, many BOS innovations were developed by city governments, U.S. states, or professional associations.“I knew there was a lot going on with this technology, but the diversity of all these fields and how closely linked they are, and the fact that we can clearly see that network through this analysis, was interesting,” Trancik says.“PV was very well-positioned to absorb innovations from other industries — thanks to the right timing, physical compatibility, and supportive policies to adapt innovations for PV applications,” Klemun adds.The analysis also reveals the role greater computing power could play in reducing BOS costs through advances like automated engineering review systems and remote site assessment software.“In terms of knowledge spillovers, what we’ve seen so far in PV may really just be the beginning,” Klemun says, pointing to the expanding role of robotics and AI-driven digital tools in driving future cost reductions and quality improvements.In addition to their qualitative analysis, the researchers demonstrated how this methodology could be used to estimate the quantitative impact of a particular innovation if one has the numerical data to plug into the cost equation.For instance, using information about material prices and manufacturing procedures, they estimate that wire sawing, a technique which was introduced in the 1980s, led to an overall PV system cost decrease of $5 per watt by reducing silicon losses and increasing throughput during fabrication.“Through this retrospective analysis, you learn something valuable for future strategy because you can see what worked and what didn’t work, and the models can also be applied prospectively. It is also useful to know what adjacent sectors may help support improvement in a particular technology,” Trancik says.Moving forward, the researchers plan to apply this methodology to a wide range of technologies, including other renewable energy systems. They also want to further study soft technology to identify innovations or processes that could accelerate cost reductions.“Although the process of technological innovation may seem like a black box, we’ve shown that you can study it just like any other phenomena,” Trancik says.This research is funded, in part, by the U.S. Department of Energy Solar Energies Technology Office. More

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      Confronting the AI/energy conundrum

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      The explosive growth of AI-powered computing centers is creating an unprecedented surge in electricity demand that threatens to overwhelm power grids and derail climate goals. At the same time, artificial intelligence technologies could revolutionize energy systems, accelerating the transition to clean power.“We’re at a cusp of potentially gigantic change throughout the economy,” said William H. Green, director of the MIT Energy Initiative (MITEI) and Hoyt C. Hottel Professor in the MIT Department of Chemical Engineering, at MITEI’s Spring Symposium, “AI and energy: Peril and promise,” held on May 13. The event brought together experts from industry, academia, and government to explore solutions to what Green described as both “local problems with electric supply and meeting our clean energy targets” while seeking to “reap the benefits of AI without some of the harms.” The challenge of data center energy demand and potential benefits of AI to the energy transition is a research priority for MITEI.AI’s startling energy demandsFrom the start, the symposium highlighted sobering statistics about AI’s appetite for electricity. After decades of flat electricity demand in the United States, computing centers now consume approximately 4 percent of the nation’s electricity. Although there is great uncertainty, some projections suggest this demand could rise to 12-15 percent by 2030, largely driven by artificial intelligence applications.Vijay Gadepally, senior scientist at MIT’s Lincoln Laboratory, emphasized the scale of AI’s consumption. “The power required for sustaining some of these large models is doubling almost every three months,” he noted. “A single ChatGPT conversation uses as much electricity as charging your phone, and generating an image consumes about a bottle of water for cooling.”Facilities requiring 50 to 100 megawatts of power are emerging rapidly across the United States and globally, driven both by casual and institutional research needs relying on large language programs such as ChatGPT and Gemini. Gadepally cited congressional testimony by Sam Altman, CEO of OpenAI, highlighting how fundamental this relationship has become: “The cost of intelligence, the cost of AI, will converge to the cost of energy.”“The energy demands of AI are a significant challenge, but we also have an opportunity to harness these vast computational capabilities to contribute to climate change solutions,” said Evelyn Wang, MIT vice president for energy and climate and the former director at the Advanced Research Projects Agency-Energy (ARPA-E) at the U.S. Department of Energy.Wang also noted that innovations developed for AI and data centers — such as efficiency, cooling technologies, and clean-power solutions — could have broad applications beyond computing facilities themselves.Strategies for clean energy solutionsThe symposium explored multiple pathways to address the AI-energy challenge. Some panelists presented models suggesting that while artificial intelligence may increase emissions in the short term, its optimization capabilities could enable substantial emissions reductions after 2030 through more efficient power systems and accelerated clean technology development.Research shows regional variations in the cost of powering computing centers with clean electricity, according to Emre Gençer, co-founder and CEO of Sesame Sustainability and former MITEI principal research scientist. Gençer’s analysis revealed that the central United States offers considerably lower costs due to complementary solar and wind resources. However, achieving zero-emission power would require massive battery deployments — five to 10 times more than moderate carbon scenarios — driving costs two to three times higher.“If we want to do zero emissions with reliable power, we need technologies other than renewables and batteries, which will be too expensive,” Gençer said. He pointed to “long-duration storage technologies, small modular reactors, geothermal, or hybrid approaches” as necessary complements.Because of data center energy demand, there is renewed interest in nuclear power, noted Kathryn Biegel, manager of R&D and corporate strategy at Constellation Energy, adding that her company is restarting the reactor at the former Three Mile Island site, now called the “Crane Clean Energy Center,” to meet this demand. “The data center space has become a major, major priority for Constellation,” she said, emphasizing how their needs for both reliability and carbon-free electricity are reshaping the power industry.Can AI accelerate the energy transition?Artificial intelligence could dramatically improve power systems, according to Priya Donti, assistant professor and the Silverman Family Career Development Professor in MIT’s Department of Electrical Engineering and Computer Science and the Laboratory for Information and Decision Systems. She showcased how AI can accelerate power grid optimization by embedding physics-based constraints into neural networks, potentially solving complex power flow problems at “10 times, or even greater, speed compared to your traditional models.”AI is already reducing carbon emissions, according to examples shared by Antonia Gawel, global director of sustainability and partnerships at Google. Google Maps’ fuel-efficient routing feature has “helped to prevent more than 2.9 million metric tons of GHG [greenhouse gas] emissions reductions since launch, which is the equivalent of taking 650,000 fuel-based cars off the road for a year,” she said. Another Google research project uses artificial intelligence to help pilots avoid creating contrails, which represent about 1 percent of global warming impact.AI’s potential to speed materials discovery for power applications was highlighted by Rafael Gómez-Bombarelli, the Paul M. Cook Career Development Associate Professor in the MIT Department of Materials Science and Engineering. “AI-supervised models can be trained to go from structure to property,” he noted, enabling the development of materials crucial for both computing and efficiency.Securing growth with sustainabilityThroughout the symposium, participants grappled with balancing rapid AI deployment against environmental impacts. While AI training receives most attention, Dustin Demetriou, senior technical staff member in sustainability and data center innovation at IBM, quoted a World Economic Forum article that suggested that “80 percent of the environmental footprint is estimated to be due to inferencing.” Demetriou emphasized the need for efficiency across all artificial intelligence applications.Jevons’ paradox, where “efficiency gains tend to increase overall resource consumption rather than decrease it” is another factor to consider, cautioned Emma Strubell, the Raj Reddy Assistant Professor in the Language Technologies Institute in the School of Computer Science at Carnegie Mellon University. Strubell advocated for viewing computing center electricity as a limited resource requiring thoughtful allocation across different applications.Several presenters discussed novel approaches for integrating renewable sources with existing grid infrastructure, including potential hybrid solutions that combine clean installations with existing natural gas plants that have valuable grid connections already in place. These approaches could provide substantial clean capacity across the United States at reasonable costs while minimizing reliability impacts.Navigating the AI-energy paradoxThe symposium highlighted MIT’s central role in developing solutions to the AI-electricity challenge.Green spoke of a new MITEI program on computing centers, power, and computation that will operate alongside the comprehensive spread of MIT Climate Project research. “We’re going to try to tackle a very complicated problem all the way from the power sources through the actual algorithms that deliver value to the customers — in a way that’s going to be acceptable to all the stakeholders and really meet all the needs,” Green said.Participants in the symposium were polled about priorities for MIT’s research by Randall Field, MITEI director of research. The real-time results ranked “data center and grid integration issues” as the top priority, followed by “AI for accelerated discovery of advanced materials for energy.”In addition, attendees revealed that most view AI’s potential regarding power as a “promise,” rather than a “peril,” although a considerable portion remain uncertain about the ultimate impact. When asked about priorities in power supply for computing facilities, half of the respondents selected carbon intensity as their top concern, with reliability and cost following. More

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      Recovering from the past and transitioning to a better energy future

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      As the frequency and severity of extreme weather events grow, it may become increasingly necessary to employ a bolder approach to climate change, warned Emily A. Carter, the Gerhard R. Andlinger Professor in Energy and the Environment at Princeton University. Carter made her case for why the energy transition is no longer enough in the face of climate change while speaking at the MIT Energy Initiative (MITEI) Presents: Advancing the Energy Transition seminar on the MIT campus.“If all we do is take care of what we did in the past — but we don’t change what we do in the future — then we’re still going to be left with very serious problems,” she said. Our approach to climate change mitigation must comprise transformation, intervention, and adaption strategies, said Carter. Transitioning to a decarbonized electricity system is one piece of the puzzle. Growing amounts of solar and wind energy — along with nuclear, hydropower, and geothermal — are slowly transforming the energy electricity landscape, but Carter noted that there are new technologies farther down the pipeline.  “Advanced geothermal may come on in the next couple of decades. Fusion will only really start to play a role later in the century, but could provide firm electricity such that we can start to decommission nuclear,” said Carter, who is also a senior strategic advisor and associate laboratory director at the Department of Energy’s Princeton Plasma Physics Laboratory. Taking this a step further, Carter outlined how this carbon-free electricity should then be used to electrify everything we can. She highlighted the industrial sector as a critical area for transformation: “The energy transition is about transitioning off of fossil fuels. If you look at the manufacturing industries, they are driven by fossil fuels right now. They are driven by fossil fuel-driven thermal processes.” Carter noted that thermal energy is much less efficient than electricity and highlighted electricity-driven strategies that could replace heat in manufacturing, such as electrolysis, plasmas, light-emitting diodes (LEDs) for photocatalysis, and joule heating. The transportation sector is also a key area for electrification, Carter said. While electric vehicles have become increasingly common in recent years, heavy-duty transportation is not as easily electrified. The solution? “Carbon-neutral fuels for heavy-duty aviation and shipping,” she said, emphasizing that these fuels will need to become part of the circular economy. “We know that when we burn those fuels, they’re going to produce CO2 [carbon dioxide] again. They need to come from a source of CO2 that is not fossil-based.” The next step is intervention in the form of carbon dioxide removal, which then necessitates methods of storage and utilization, according to Carter. “There’s a lot of talk about building large numbers of pipelines to capture the CO2 — from fossil fuel-driven power plants, cement plants, steel plants, all sorts of industrial places that emit CO2 — and then piping it and storing it in underground aquifers,” she explained. Offshore pipelines are much more expensive than those on land, but can mitigate public concerns over their safety. Europe is exclusively focusing their efforts offshore for this very reason, and the same could be true for the United States, Carter said.  Once carbon dioxide is captured, commercial utilization may provide economic leverage to accelerate sequestration, even if only a few gigatons are used per year, Carter noted. Through mineralization, CO2 can be converted into carbonates, which could be used in building materials such as concrete and road-paving materials.  There is another form of intervention that Carter currently views as a last resort: solar geoengineering, sometimes known as solar radiation management or SRM. In 1991, Mount Pinatubo in the Philippines erupted and released sulfur dioxide into the stratosphere, which caused a temporary cooling of the Earth by approximately 0.5 degree Celsius for over a year. SRM seeks to recreate that cooling effect by injecting particles into the atmosphere that reflect sunlight. According to Carter, there are three main strategies: stratospheric aerosol injection, cirrus cloud thinning (thinning clouds to let more infrared radiation emitted by the earth escape to space), and marine cloud brightening (brightening clouds with sea salt so they reflect more light).  “My view is, I hope we don’t ever have to do it, but I sure think we should understand what would happen in case somebody else just decides to do it. It’s a global security issue,” said Carter. “In principle, it’s not so difficult technologically, so we’d like to really understand and to be able to predict what would happen if that happened.” With any technology, stakeholder and community engagement is essential for deployment, Carter said. She emphasized the importance of both respectfully listening to concerns and thoroughly addressing them, stating, “Hopefully, there’s enough information given to assuage their fears. We have to gain the trust of people before any deployment can be considered.” A crucial component of this trust starts with the responsibility of the scientific community to be transparent and critique each other’s work, Carter said. “Skepticism is good. You should have to prove your proof of principle.” MITEI Presents: Advancing the Energy Transition is an MIT Energy Initiative speaker series highlighting energy experts and leaders at the forefront of the scientific, technological, and policy solutions needed to transform our energy systems. The series will continue in fall 2025. For more information on this and additional events, visit the MITEI website. More