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    The brain power behind sustainable AI

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    How can you use science to build a better gingerbread house?That was something Miranda Schwacke spent a lot of time thinking about. The MIT graduate student in the Department of Materials Science and Engineering (DMSE) is part of Kitchen Matters, a group of grad students who use food and kitchen tools to explain scientific concepts through short videos and outreach events. Past topics included why chocolate “seizes,” or becomes difficult to work with when melting (spoiler: water gets in), and how to make isomalt, the sugar glass that stunt performers jump through in action movies.Two years ago, when the group was making a video on how to build a structurally sound gingerbread house, Schwacke scoured cookbooks for a variable that would produce the most dramatic difference in the cookies.“I was reading about what determines the texture of cookies, and then tried several recipes in my kitchen until I got two gingerbread recipes that I was happy with,” Schwacke says.She focused on butter, which contains water that turns to steam at high baking temperatures, creating air pockets in cookies. Schwacke predicted that decreasing the amount of butter would yield denser gingerbread, strong enough to hold together as a house.“This hypothesis is an example of how changing the structure can influence the properties and performance of material,” Schwacke said in the eight-minute video.That same curiosity about materials properties and performance drives her research on the high energy cost of computing, especially for artificial intelligence. Schwacke develops new materials and devices for neuromorphic computing, which mimics the brain by processing and storing information in the same place. She studies electrochemical ionic synapses — tiny devices that can be “tuned” to adjust conductivity, much like neurons strengthening or weakening connections in the brain.“If you look at AI in particular — to train these really large models — that consumes a lot of energy. And if you compare that to the amount of energy that we consume as humans when we’re learning things, the brain consumes a lot less energy,” Schwacke says. “That’s what led to this idea to find more brain-inspired, energy-efficient ways of doing AI.”Her advisor, Bilge Yildiz, underscores the point: One reason the brain is so efficient is that data doesn’t need to be moved back and forth.“In the brain, the connections between our neurons, called synapses, are where we process information. Signal transmission is there. It is processed, programmed, and also stored in the same place,” says Yildiz, the Breene M. Kerr (1951) Professor in the Department of Nuclear Science and Engineering and DMSE. Schwacke’s devices aim to replicate that efficiency.Scientific rootsThe daughter of a marine biologist mom and an electrical engineer dad, Schwacke was immersed in science from a young age. Science was “always a part of how I understood the world.”“I was obsessed with dinosaurs. I wanted to be a paleontologist when I grew up,” she says. But her interests broadened. At her middle school in Charleston, South Carolina, she joined a FIRST Lego League robotics competition, building robots to complete tasks like pushing or pulling objects. “My parents, my dad especially, got very involved in the school team and helping us design and build our little robot for the competition.”Her mother, meanwhile, studied how dolphin populations are affected by pollution for the National Oceanic and Atmospheric Administration. That had a lasting impact.“That was an example of how science can be used to understand the world, and also to figure out how we can improve the world,” Schwacke says. “And that’s what I’ve always wanted to do with science.”Her interest in materials science came later, in her high school magnet program. There, she was introduced to the interdisciplinary subject, a blend of physics, chemistry, and engineering that studies the structure and properties of materials and uses that knowledge to design new ones.“I always liked that it goes from this very basic science, where we’re studying how atoms are ordering, all the way up to these solid materials that we interact with in our everyday lives — and how that gives them their properties that we can see and play with,” Schwacke says.As a senior, she participated in a research program with a thesis project on dye-sensitized solar cells, a low-cost, lightweight solar technology that uses dye molecules to absorb light and generate electricity.“What drove me was really understanding, this is how we go from light to energy that we can use — and also seeing how this could help us with having more renewable energy sources,” Schwacke says.After high school, she headed across the country to Caltech. “I wanted to try a totally new place,” she says, where she studied materials science, including nanostructured materials thousands of times thinner than a human hair. She focused on materials properties and microstructure — the tiny internal structure that governs how materials behave — which led her to electrochemical systems like batteries and fuel cells.AI energy challengeAt MIT, she continued exploring energy technologies. She met Yildiz during a Zoom meeting in her first year of graduate school, in fall 2020, when the campus was still operating under strict Covid-19 protocols. Yildiz’s lab studies how charged atoms, or ions, move through materials in technologies like fuel cells, batteries, and electrolyzers.The lab’s research into brain-inspired computing fired Schwacke’s imagination, but she was equally drawn to Yildiz’s way of talking about science.“It wasn’t based on jargon and emphasized a very basic understanding of what was going on — that ions are going here, and electrons are going here — to understand fundamentally what’s happening in the system,” Schwacke says.That mindset shaped her approach to research. Her early projects focused on the properties these devices need to work well — fast operation, low energy use, and compatibility with semiconductor technology — and on using magnesium ions instead of hydrogen, which can escape into the environment and make devices unstable.Her current project, the focus of her PhD thesis, centers on understanding how the insertion of magnesium ions into tungsten oxide, a metal oxide whose electrical properties can be precisely tuned, changes its electrical resistance. In these devices, tungsten oxide serves as a channel layer, where resistance controls signal strength, much like synapses regulate signals in the brain.“I am trying to understand exactly how these devices change the channel conductance,” Schwacke says.Schwacke’s research was recognized with a MathWorks Fellowship from the School of Engineering in 2023 and 2024. The fellowship supports graduate students who leverage tools like MATLAB or Simulink in their work; Schwacke applied MATLAB for critical data analysis and visualization.Yildiz describes Schwacke’s research as a novel step toward solving one of AI’s biggest challenges.“This is electrochemistry for brain-inspired computing,” Yildiz says. “It’s a new context for electrochemistry, but also with an energy implication, because the energy consumption of computing is unsustainably increasing. We have to find new ways of doing computing with much lower energy, and this is one way that can help us move in that direction.”Like any pioneering work, it comes with challenges, especially in bridging the concepts between electrochemistry and semiconductor physics.“Our group comes from a solid-state chemistry background, and when we started this work looking into magnesium, no one had used magnesium in these kinds of devices before,” Schwacke says. “So we were looking at the magnesium battery literature for inspiration and different materials and strategies we could use. When I started this, I wasn’t just learning the language and norms for one field — I was trying to learn it for two fields, and also translate between the two.”She also grapples with a challenge familiar to all scientists: how to make sense of messy data.“The main challenge is being able to take my data and know that I’m interpreting it in a way that’s correct, and that I understand what it actually means,” Schwacke says.She overcomes hurdles by collaborating closely with colleagues across fields, including neuroscience and electrical engineering, and sometimes by just making small changes to her experiments and watching what happens next.Community mattersSchwacke is not just active in the lab. In Kitchen Matters, she and her fellow DMSE grad students set up booths at local events like the Cambridge Science Fair and Steam It Up, an after-school program with hands-on activities for kids.“We did ‘pHun with Food’ with ‘fun’ spelled with a pH, so we had cabbage juice as a pH indicator,” Schwacke says. “We let the kids test the pH of lemon juice and vinegar and dish soap, and they had a lot of fun mixing the different liquids and seeing all the different colors.”She has also served as the social chair and treasurer for DMSE’s graduate student group, the Graduate Materials Council. As an undergraduate at Caltech, she led workshops in science and technology for Robogals, a student-run group that encourages young women to pursue careers in science, and assisted students in applying for the school’s Summer Undergraduate Research Fellowships.For Schwacke, these experiences sharpened her ability to explain science to different audiences, a skill she sees as vital whether she’s presenting at a kids’ fair or at a research conference.“I always think, where is my audience starting from, and what do I need to explain before I can get into what I’m doing so that it’ll all make sense to them?” she says.Schwacke sees the ability to communicate as central to building community, which she considers an important part of doing research. “It helps with spreading ideas. It always helps to get a new perspective on what you’re working on,” she says. “I also think it keeps us sane during our PhD.”Yildiz sees Schwacke’s community involvement as an important part of her resume. “She’s doing all these activities to motivate the broader community to do research, to be interested in science, to pursue science and technology, but that ability will help her also progress in her own research and academic endeavors.”After her PhD, Schwacke wants to take that ability to communicate with her to academia, where she’d like to inspire the next generation of scientists and engineers. Yildiz has no doubt she’ll thrive.“I think she’s a perfect fit,” Yildiz says. “She’s brilliant, but brilliance by itself is not enough. She’s persistent, resilient. You really need those on top of that.” More

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      MIT Maritime Consortium releases “Nuclear Ship Safety Handbook”

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      Commercial shipping accounts for 3 percent of all greenhouse gas emissions globally. As the sector sets climate goals and chases a carbon-free future, nuclear power — long used as a source for military vessels — presents an enticing solution. To date, however, there has been no clear, unified public document available to guide design safety for certain components of civilian nuclear ships. A new “Nuclear Ship Safety Handbook” by the MIT Maritime Consortium aims to change that and set the standard for safe maritime nuclear propulsion.“This handbook is a critical tool in efforts to support the adoption of nuclear in the maritime industry,” explains Themis Sapsis, the William I. Koch Professor of Mechanical Engineering at MIT, director of the MIT Center for Ocean Engineering, and co-director of the MIT Maritime Consortium. “The goal is to provide a strong basis for initial safety on key areas that require nuclear and maritime regulatory research and development in the coming years to prepare for nuclear propulsion in the maritime industry.”Using research data and standards, combined with operational experiences during civilian maritime nuclear operations, the handbook provides unique insights into potential issues and resolutions in the design efficacy of maritime nuclear operations, a topic of growing importance on the national and international stage. “Right now, the nuclear-maritime policies that exist are outdated and often tied only to specific technologies, like pressurized water reactors,” says Jose Izurieta, a graduate student in the Department of Mechanical Engineering (MechE) Naval Construction and Engineering (2N) Program, and one of the handbook authors. “With the recent U.K.-U.S. Technology Prosperity Deal now including civil maritime nuclear applications, I hope the handbook can serve as a foundation for creating a clear, modern regulatory framework for nuclear-powered commercial ships.”The recent memorandum of understanding signed by the U.S. and U.K calls for the exploration of “novel applications of advanced nuclear energy, including civil maritime applications,” and for the parties to play “a leading role informing the establishment of international standards, potential establishment of a maritime shipping corridor between the Participants’ territories, and strengthening energy resilience for the Participants’ defense facilities.”“The U.S.-U.K. nuclear shipping corridor offers a great opportunity to collaborate with legislators on establishing the critical framework that will enable the United States to invest on nuclear-powered merchant vessels — an achievement that will reestablish America in the shipbuilding space,” says Fotini Christia, the Ford International Professor of the Social Sciences, director of the Institute for Data, Systems, and Society (IDSS), director of the MIT Sociotechnical Systems Research Center, and co-director of the MIT Maritime Consortium.“With over 30 nations now building or planning their first reactors, nuclear energy’s global acceptance is unprecedented — and that momentum is key to aligning safety rules across borders for nuclear-powered ships and the respective ports,” says Koroush Shirvan, the Atlantic Richfield Career Development Professor in Energy Studies at MIT and director of the Reactor Technology Course for Utility Executives.The handbook, which is divided into chapters in areas involving the overlapping nuclear and maritime safety design decisions that will be encountered by engineers, is careful to balance technical and practical guidance with policy considerations.Commander Christopher MacLean, MIT associate professor of the practice in mechanical engineering, naval construction, and engineering, says the handbook will significantly benefit the entire maritime community, specifically naval architects and marine engineers, by providing standardized guidelines for design and operation specific to nuclear powered commercial vessels.“This will assist in enhancing safety protocols, improve risk assessments, and ensure consistent compliance with international regulations,” MacLean says. “This will also help foster collaboration amongst engineers and regulators. Overall, this will further strengthen the reliability, sustainability, and public trust in nuclear-powered maritime systems.”Anthony Valiaveedu, the handbook’s lead author, and co-author Nat Edmonds, are both students in the MIT Master’s Program in Technology and Policy (TPP) within the IDSS. The pair are also co-authors of a paper published in Science Policy Review earlier this year that offered structured advice on the development of nuclear regulatory policies.“It is important for safety and technology to go hand-in-hand,” Valiaveedu explains. “What we have done is provide a risk-informed process to begin these discussions for engineers and policymakers.”“Ultimately, I hope this framework can be used to build strong bilateral agreements between nations that will allow nuclear propulsion to thrive,” says fellow co-author Izurieta.Impact on industry“Maritime designers needed a source of information to improve their ability to understand and design the reactor primary components, and development of the ‘Nuclear Ship Safety Handbook’ was a good step to bridge this knowledge gap,” says Christopher J. Wiernicki, American Bureau of Shipping (ABS) chair and CEO. “For this reason, it is an important document for the industry.”The ABS, which is the American classification society for the maritime industry, develops criteria and provides safety certification for all ocean-going vessels. ABS is among the founding members of the MIT Maritime Consortium. Capital Clean Energy Carriers Corp., HD Korea Shipbuilding and Offshore Engineering, and Delos Navigation Ltd. are also consortium founding members. Innovation members are Foresight-Group, Navios Maritime Partners L.P., Singapore Maritime Institute, and Dorian LPG.“As we consider a net-zero framework for the shipping industry, nuclear propulsion represents a potential solution. Careful investigation remains the priority, with safety and regulatory standards at the forefront,” says Jerry Kalogiratos, CEO of Capital Clean Energy Carriers Corp. “As first movers, we are exploring all options. This handbook lays the technical foundation for the development of nuclear-powered commercial vessels.”Sangmin Park, senior vice president at HD Korea Shipbuilding and Offshore Engineering, says “The ‘Nuclear Ship Safety Handbook’ marks a groundbreaking milestone that bridges shipbuilding excellence and nuclear safety. It drives global collaboration between industry and academia, and paves the way for the safe advancement of the nuclear maritime era.”Maritime at MITMIT has been a leading center of ship research and design for over a century, with work at the Institute today representing significant advancements in fluid mechanics and hydrodynamics, acoustics, offshore mechanics, marine robotics and sensors, and ocean sensing and forecasting. Maritime Consortium projects, including the handbook, reflect national priorities aimed at revitalizing the U.S. shipbuilding and commercial maritime industries.The MIT Maritime Consortium, which launched in 2024, brings together MIT and maritime industry leaders to explore data-powered strategies to reduce harmful emissions, optimize vessel operations, and support economic priorities.“One of our most important efforts is the development of technologies, policies, and regulations to make nuclear propulsion for commercial ships a reality,” says Sapsis. “Over the last year, we have put together an interdisciplinary team with faculty and students from across the Institute. One of the outcomes of this effort is this very detailed document providing detailed guidance on how such effort should be implemented safely.”Handbook contributors come from multiple disciplines and MIT departments, labs, and research centers, including the Center for Ocean Engineering, IDSS, MechE’s Course 2N Program, the MIT Technology and Policy Program, and the Department of Nuclear Science and Engineering.MIT faculty members and research advisors on the project include Sapsis; Christia; Shirvan; MacLean; Jacopo Buongiorno, the Battelle Energy Alliance Professor in Nuclear Science and Engineering, director, Center for Advanced Nuclear Energy Systems, and director of science and technology for the Nuclear Reactor Laboratory; and Captain Andrew Gillespy, professor of the practice and director of the Naval Construction and Engineering (2N) Program.“Proving the viability of nuclear propulsion for civilian ships will entail getting the technologies, the economics and the regulations right,” says Buongiorno. “This handbook is a meaningful initial contribution to the development of a sound regulatory framework.”“We were lucky to have a team of students and knowledgeable professors from so many fields,” says Edmonds. “Before even beginning the outline of the handbook, we did significant archival and history research to understand the existing regulations and overarching story of nuclear ships. Some of the most relevant documents we found were written before 1975, and many of them were stored in the bellows of the NS Savannah.”The NS Savannah, which was built in the late 1950s as a demonstration project for the potential peacetime uses of nuclear energy, was the first nuclear-powered merchant ship. The Savannah was first launched on July 21, 1959, two years after the first nuclear-powered civilian vessel, the Soviet ice-breaker Lenin, and was retired in 1971.Historical context for this project is important, because the reactor technologies envisioned for maritime propulsion today are quite different from the traditional pressurized water reactors used by the U.S. Navy. These new reactors are being developed not just in the maritime context, but also to power ports and data centers on land; they all use low-enriched uranium and are passively cooled. For the maritime industry, Sapsis says, “the technology is there, it’s safe, and it’s ready.”“The Nuclear Ship Safety Handbook” is publicly available on the MIT Maritime Consortium website and from the MIT Libraries.  More

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      AI system learns from many types of scientific information and runs experiments to discover new materials

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      Machine-learning models can speed up the discovery of new materials by making predictions and suggesting experiments. But most models today only consider a few specific types of data or variables. Compare that with human scientists, who work in a collaborative environment and consider experimental results, the broader scientific literature, imaging and structural analysis, personal experience or intuition, and input from colleagues and peer reviewers.Now, MIT researchers have developed a method for optimizing materials recipes and planning experiments that incorporates information from diverse sources like insights from the literature, chemical compositions, microstructural images, and more. The approach is part of a new platform, named Copilot for Real-world Experimental Scientists (CRESt), that also uses robotic equipment for high-throughput materials testing, the results of which are fed back into large multimodal models to further optimize materials recipes.Human researchers can converse with the system in natural language, with no coding required, and the system makes its own observations and hypotheses along the way. Cameras and visual language models also allow the system to monitor experiments, detect issues, and suggest corrections.“In the field of AI for science, the key is designing new experiments,” says Ju Li, School of Engineering Carl Richard Soderberg Professor of Power Engineering. “We use multimodal feedback — for example information from previous literature on how palladium behaved in fuel cells at this temperature, and human feedback — to complement experimental data and design new experiments. We also use robots to synthesize and characterize the material’s structure and to test performance.”The system is described in a paper published in Nature. The researchers used CRESt to explore more than 900 chemistries and conduct 3,500 electrochemical tests, leading to the discovery of a catalyst material that delivered record power density in a fuel cell that runs on formate salt to produce electricity.Joining Li on the paper as first authors are PhD student Zhen Zhang, Zhichu Ren PhD ’24, PhD student Chia-Wei Hsu, and postdoc Weibin Chen. Their coauthors are MIT Assistant Professor Iwnetim Abate; Associate Professor Pulkit Agrawal; JR East Professor of Engineering Yang Shao-Horn; MIT.nano researcher Aubrey Penn; Zhang-Wei Hong PhD ’25, Hongbin Xu PhD ’25; Daniel Zheng PhD ’25; MIT graduate students Shuhan Miao and Hugh Smith; MIT postdocs Yimeng Huang, Weiyin Chen, Yungsheng Tian, Yifan Gao, and Yaoshen Niu; former MIT postdoc Sipei Li; and collaborators including Chi-Feng Lee, Yu-Cheng Shao, Hsiao-Tsu Wang, and Ying-Rui Lu.

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      A smarter systemMaterials science experiments can be time-consuming and expensive. They require researchers to carefully design workflows, make new material, and run a series of tests and analysis to understand what happened. Those results are then used to decide how to improve the material.To improve the process, some researchers have turned to a machine-learning strategy known as active learning to make efficient use of previous experimental data points and explore or exploit those data. When paired with a statistical technique known as Bayesian optimization (BO), active learning has helped researchers identify new materials for things like batteries and advanced semiconductors.“Bayesian optimization is like Netflix recommending the next movie to watch based on your viewing history, except instead it recommends the next experiment to do,” Li explains. “But basic Bayesian optimization is too simplistic. It uses a boxed-in design space, so if I say I’m going to use platinum, palladium, and iron, it only changes the ratio of those elements in this small space. But real materials have a lot more dependencies, and BO often gets lost.”Most active learning approaches also rely on single data streams that don’t capture everything that goes on in an experiment. To equip computational systems with more human-like knowledge, while still taking advantage of the speed and control of automated systems, Li and his collaborators built CRESt.CRESt’s robotic equipment includes a liquid-handling robot, a carbothermal shock system to rapidly synthesize materials, an automated electrochemical workstation for testing, characterization equipment including automated electron microscopy and optical microscopy, and auxiliary devices such as pumps and gas valves, which can also be remotely controlled.  Many processing parameters can also be tuned.With the user interface, researchers can chat with CRESt and tell it to use active learning to find promising materials recipes for different projects. CRESt can include up to 20 precursor molecules and substrates into its recipe. To guide material designs, CRESt’s models search through scientific papers for descriptions of elements or precursor molecules that might be useful. When human researchers tell CRESt to pursue new recipes, it kicks off a robotic symphony of sample preparation, characterization, and testing. The researcher can also ask CRESt to perform image analysis from scanning electron microscopy imaging, X-ray diffraction, and other sources.Information from those processes is used to train the active learning models, which use both literature knowledge and current experimental results to suggest further experiments and accelerate materials discovery.“For each recipe we use previous literature text or databases, and it creates these huge representations of every recipe based on the previous knowledge base before even doing the experiment,” says Li. “We perform principal component analysis in this knowledge embedding space to get a reduced search space that captures most of the performance variability. Then we use Bayesian optimization in this reduced space to design the new experiment. After the new experiment, we feed newly acquired multimodal experimental data and human feedback into a large language model to augment the knowledgebase and redefine the reduced search space, which gives us a big boost in active learning efficiency.”Materials science experiments can also face reproducibility challenges. To address the problem, CRESt monitors its experiments with cameras, looking for potential problems and suggesting solutions via text and voice to human researchers.The researchers used CRESt to develop an electrode material for an advanced type of high-density fuel cell known as a direct formate fuel cell. After exploring more than 900 chemistries over three months, CRESt discovered a catalyst material made from eight elements that achieved a 9.3-fold improvement in power density per dollar over pure palladium, an expensive precious metal. In further tests, CRESTs material was used to deliver a record power density to a working direct formate fuel cell even though the cell contained just one-fourth of the precious metals of previous devices.The results show the potential for CRESt to find solutions to real-world energy problems that have plagued the materials science and engineering community for decades.“A significant challenge for fuel-cell catalysts is the use of precious metal,” says Zhang. “For fuel cells, researchers have used various precious metals like palladium and platinum. We used a multielement catalyst that also incorporates many other cheap elements to create the optimal coordination environment for catalytic activity and resistance to poisoning species such as carbon monoxide and adsorbed hydrogen atom. People have been searching low-cost options for many years. This system greatly accelerated our search for these catalysts.”A helpful assistantEarly on, poor reproducibility emerged as a major problem that limited the researchers’ ability to perform their new active learning technique on experimental datasets. Material properties can be influenced by the way the precursors are mixed and processed, and any number of problems can subtly alter experimental conditions, requiring careful inspection to correct.To partially automate the process, the researchers coupled computer vision and vision language models with domain knowledge from the scientific literature, which allowed the system to hypothesize sources of irreproducibility and propose solutions. For example, the models can notice when there’s a millimeter-sized deviation in a sample’s shape or when a pipette moves something out of place. The researchers incorporated some of the model’s suggestions, leading to improved consistency, suggesting the models already make good experimental assistants.The researchers noted that humans still performed most of the debugging in their experiments.“CREST is an assistant, not a replacement, for human researchers,” Li says. “Human researchers are still indispensable. In fact, we use natural language so the system can explain what it is doing and present observations and hypotheses. But this is a step toward more flexible, self-driving labs.” 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 tool makes generative AI models more likely to create breakthrough materials

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      The artificial intelligence models that turn text into images are also useful for generating new materials. Over the last few years, generative materials models from companies like Google, Microsoft, and Meta have drawn on their training data to help researchers design tens of millions of new materials.But when it comes to designing materials with exotic quantum properties like superconductivity or unique magnetic states, those models struggle. That’s too bad, because humans could use the help. For example, after a decade of research into a class of materials that could revolutionize quantum computing, called quantum spin liquids, only a dozen material candidates have been identified. The bottleneck means there are fewer materials to serve as the basis for technological breakthroughs.Now, MIT researchers have developed a technique that lets popular generative materials models create promising quantum materials by following specific design rules. The rules, or constraints, steer models to create materials with unique structures that give rise to quantum properties.“The models from these large companies generate materials optimized for stability,” says Mingda Li, MIT’s Class of 1947 Career Development Professor. “Our perspective is that’s not usually how materials science advances. We don’t need 10 million new materials to change the world. We just need one really good material.”The approach is described today in a paper published by Nature Materials. The researchers applied their technique to generate millions of candidate materials consisting of geometric lattice structures associated with quantum properties. From that pool, they synthesized two actual materials with exotic magnetic traits.“People in the quantum community really care about these geometric constraints, like the Kagome lattices that are two overlapping, upside-down triangles. We created materials with Kagome lattices because those materials can mimic the behavior of rare earth elements, so they are of high technical importance.” Li says.Li is the senior author of the paper. His MIT co-authors include PhD students Ryotaro Okabe, Mouyang Cheng, Abhijatmedhi Chotrattanapituk, and Denisse Cordova Carrizales; postdoc Manasi Mandal; undergraduate researchers Kiran Mak and Bowen Yu; visiting scholar Nguyen Tuan Hung; Xiang Fu ’22, PhD ’24; and professor of electrical engineering and computer science Tommi Jaakkola, who is an affiliate of the Computer Science and Artificial Intelligence Laboratory (CSAIL) and Institute for Data, Systems, and Society. Additional co-authors include Yao Wang of Emory University, Weiwei Xie of Michigan State University, YQ Cheng of Oak Ridge National Laboratory, and Robert Cava of Princeton University.Steering models toward impactA material’s properties are determined by its structure, and quantum materials are no different. Certain atomic structures are more likely to give rise to exotic quantum properties than others. For instance, square lattices can serve as a platform for high-temperature superconductors, while other shapes known as Kagome and Lieb lattices can support the creation of materials that could be useful for quantum computing.To help a popular class of generative models known as a diffusion models produce materials that conform to particular geometric patterns, the researchers created SCIGEN (short for Structural Constraint Integration in GENerative model). SCIGEN is a computer code that ensures diffusion models adhere to user-defined constraints at each iterative generation step. With SCIGEN, users can give any generative AI diffusion model geometric structural rules to follow as it generates materials.AI diffusion models work by sampling from their training dataset to generate structures that reflect the distribution of structures found in the dataset. SCIGEN blocks generations that don’t align with the structural rules.To test SCIGEN, the researchers applied it to a popular AI materials generation model known as DiffCSP. They had the SCIGEN-equipped model generate materials with unique geometric patterns known as Archimedean lattices, which are collections of 2D lattice tilings of different polygons. Archimedean lattices can lead to a range of quantum phenomena and have been the focus of much research.“Archimedean lattices give rise to quantum spin liquids and so-called flat bands, which can mimic the properties of rare earths without rare earth elements, so they are extremely important,” says Cheng, a co-corresponding author of the work. “Other Archimedean lattice materials have large pores that could be used for carbon capture and other applications, so it’s a collection of special materials. In some cases, there are no known materials with that lattice, so I think it will be really interesting to find the first material that fits in that lattice.”The model generated over 10 million material candidates with Archimedean lattices. One million of those materials survived a screening for stability. Using the supercomputers in Oak Ridge National Laboratory, the researchers then took a smaller sample of 26,000 materials and ran detailed simulations to understand how the materials’ underlying atoms behaved. The researchers found magnetism in 41 percent of those structures.From that subset, the researchers synthesized two previously undiscovered compounds, TiPdBi and TiPbSb, at Xie and Cava’s labs. Subsequent experiments showed the AI model’s predictions largely aligned with the actual material’s properties.“We wanted to discover new materials that could have a huge potential impact by incorporating these structures that have been known to give rise to quantum properties,” says Okabe, the paper’s first author. “We already know that these materials with specific geometric patterns are interesting, so it’s natural to start with them.”Accelerating material breakthroughsQuantum spin liquids could unlock quantum computing by enabling stable, error-resistant qubits that serve as the basis of quantum operations. But no quantum spin liquid materials have been confirmed. Xie and Cava believe SCIGEN could accelerate the search for these materials.“There’s a big search for quantum computer materials and topological superconductors, and these are all related to the geometric patterns of materials,” Xie says. “But experimental progress has been very, very slow,” Cava adds. “Many of these quantum spin liquid materials are subject to constraints: They have to be in a triangular lattice or a Kagome lattice. If the materials satisfy those constraints, the quantum researchers get excited; it’s a necessary but not sufficient condition. So, by generating many, many materials like that, it immediately gives experimentalists hundreds or thousands more candidates to play with to accelerate quantum computer materials research.”“This work presents a new tool, leveraging machine learning, that can predict which materials will have specific elements in a desired geometric pattern,” says Drexel University Professor Steve May, who was not involved in the research. “This should speed up the development of previously unexplored materials for applications in next-generation electronic, magnetic, or optical technologies.”The researchers stress that experimentation is still critical to assess whether AI-generated materials can be synthesized and how their actual properties compare with model predictions. Future work on SCIGEN could incorporate additional design rules into generative models, including chemical and functional constraints.“People who want to change the world care about material properties more than the stability and structure of materials,” Okabe says. “With our approach, the ratio of stable materials goes down, but it opens the door to generate a whole bunch of promising materials.”The work was supported, in part, by the U.S. Department of Energy, the National Energy Research Scientific Computing Center, the National Science Foundation, and Oak Ridge National Laboratory. More

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      Working to make fusion a viable energy source

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      George Tynan followed a nonlinear path to fusion.Following his undergraduate degree in aerospace engineering, Tynann’s work in the industry spurred his interest in rocket propulsion technology. Because most methods for propulsion involve the manipulation of hot ionized matter, or plasmas, Tynan focused his attention on plasma physics.It was then that he realized that plasmas could also drive nuclear fusion. “As a potential energy source, it could really be transformative, and the idea that I could work on something that could have that kind of impact on the future was really attractive to me,” he says.That same drive, to realize the promise of fusion by researching both plasma physics and fusion engineering, drives Tynan today. It’s work he will be pursuing as the Norman C. Rasmussen Adjunct Professor in the Department of Nuclear Science and Engineering (NSE) at MIT.An early interest in fluid flowTynan’s enthusiasm for science and engineering traces back to his childhood. His electrical engineer father found employment in the U.S. space program and moved the family to Cape Canaveral in Florida.“This was in the ’60s, when we were launching Saturn V to the moon, and I got to watch all the launches from the beach,” Tynan remembers. That experience was formative and Tynan became fascinated with how fluids flow.“I would stick my hand out the window and pretend it was an airplane wing and tilt it with oncoming wind flow and see how the force would change on my hand,” Tynan laughs. The interest eventually led to an undergraduate degree in aerospace engineering at California State Polytechnic University in Pomona.The switch to a new career would happen after work in the private sector, when Tynan discovered an interest in the use of plasmas for propulsion systems. He moved to the University of California at Los Angeles for graduate school, and it was here that the realization that plasmas could also anchor fusion moved Tynan into this field.This was in the ’80s, when climate change was not as much in the public consciousness as it is today. Even so, “I knew there’s not an infinite amount of oil and gas around, and that at some point we would have to have widespread adoption of nuclear-based sources,” Tynan remembers. He was also attracted by the sustained effort it would take to make fusion a reality.Doctoral workTo create energy from fusion, it’s important to get an accurate measurement of the “energy confinement time,” which is a measure of how long it takes for the hot fuel to cool down when all heat sources are turned off. When Tynan started graduate school, this measure was still an empirical guess. He decided to focus his research on the physics of observable confinement time.It was during this doctoral research that Tynan was able to study the fundamental differences in the behavior of turbulence in plasma as compared to conventional fluids. Typically, when an ordinary fluid is stirred with increasing vigor, the fluid’s motion eventually becomes chaotic or turbulent. However, plasmas can act in a surprising way: confined plasmas, when heated sufficiently strongly, would spontaneously quench the turbulent transport at the boundary of the plasmaAn experiment in Germany had unexpectedly discovered this plasma behavior. While subsequent work on other experimental devices confirmed this surprising finding, all earlier experiments lacked the ability to measure the turbulence in detail.Brian LaBombard, now a senior research scientist at MIT’s Plasma Science and Fusion Center (PSFC), was a postdoc at UCLA at the time. Under LaBombard’s direction, Tynan developed a set of Langmuir probes, which are reasonably simple diagnostics for plasma turbulence studies, to further investigate this unusual phenomenon. It formed the basis for his doctoral dissertation. “I happened to be at the right place at the right time so I could study this turbulence quenching phenomenon in much more detail than anyone else could, up until that time,” Tynan says.As a PhD student and then postdoc, Tynan studied the phenomenon in depth, shuttling between research facilities in Germany, Princeton University’s Plasma Physics Laboratory, and UCLA.Fusion at UCSDAfter completing his doctorate and postdoctoral work, Tynan worked at a startup for a few years when he learned that the University of California at San Diego was launching a new fusion research group at the engineering school. When they reached out, Tynan joined the faculty and built a research program focused on plasma turbulence and plasma-material interactions in fusion systems. Eventually, he became associate dean of engineering, and later, chair of the Department of Mechanical and Aerospace Engineering, serving in these roles for nearly a decade.Tynan visited MIT on sabbatical in 2023, when his conversations with NSE faculty members Dennis Whyte, Zach Hartwig, and Michael Short excited him about the challenges the private sector faces in making fusion a reality. He saw opportunities to solve important problems at MIT that complemented his work at UC San Diego.Tynan is excited to tackle what he calls, “the big physics and engineering challenges of fusion plasmas” at NSE: how to remove the heat and exhaust generated by burning plasma so it doesn’t damage the walls of the fusion device and the plasma does not choke on the helium ash. He also hopes to explore robust engineering solutions for practical fusion energy, with a particular focus on developing better materials for use in fusion devices that will make them longer-lasting, while  minimizing the production of radioactive waste.“Ten or 15 years ago, I was somewhat pessimistic that I would ever see commercial exploitation of fusion in my lifetime,” Tynan says. But that outlook has changed, as he has seen collaborations between MIT and Commonwealth Fusion Systems (CFS) and other private-sector firms that seek to accelerate the timeline to the deployment of fusion in the real world.In 2021, for example, MIT’s PSFC and CFS took a significant step toward commercial carbon-free power generation. They designed and built a high-temperature superconducting magnet, the strongest fusion magnet in the world.The milestone was especially exciting because the promise of realizing the dream of fusion energy now felt closer. And being at MIT “seemed like a really quick way to get deeply connected with what’s going on in the efforts to develop fusion energy,” Tynan says.In addition, “while on sabbatical at MIT, I saw how quickly research staff and students can capitalize on a suggestion of a new idea, and that intrigued me,” he adds.Tynan brings his special blend of expertise to the table. In addition to extensive experience in plasma physics, he has spent a lot more time on hardcore engineering issues like materials, as well. “The key is to integrate the whole thing into a workable and viable system,” Tynan 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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      Study sheds light on graphite’s lifespan in nuclear reactors

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      Graphite is a key structural component in some of the world’s oldest nuclear reactors and many of the next-generation designs being built today. But it also condenses and swells in response to radiation — and the mechanism behind those changes has proven difficult to study.Now, MIT researchers and collaborators have uncovered a link between properties of graphite and how the material behaves in response to radiation. The findings could lead to more accurate, less destructive ways of predicting the lifespan of graphite materials used in reactors around the world.“We did some basic science to understand what leads to swelling and, eventually, failure in graphite structures,” says MIT Research Scientist Boris Khaykovich, senior author of the new study. “More research will be needed to put this into practice, but the paper proposes an attractive idea for industry: that you might not need to break hundreds of irradiated samples to understand their failure point.”Specifically, the study shows a connection between the size of the pores within graphite and the way the material swells and shrinks in volume, leading to degradation.“The lifetime of nuclear graphite is limited by irradiation-induced swelling,” says co-author and MIT Research Scientist Lance Snead. “Porosity is a controlling factor in this swelling, and while graphite has been extensively studied for nuclear applications since the Manhattan Project, we still do not have a clear understanding of the porosity in both mechanical properties and swelling. This work addresses that.”The open-access paper appears this week in Interdisciplinary Materials. It is co-authored by Khaykovich, Snead, MIT Research Scientist Sean Fayfar, former MIT research fellow Durgesh Rai, Stony Brook University Assistant Professor David Sprouster, Oak Ridge National Laboratory Staff Scientist Anne Campbell, and Argonne National Laboratory Physicist Jan Ilavsky.A long-studied, complex materialEver since 1942, when physicists and engineers built the world’s first nuclear reactor on a converted squash court at the University of Chicago, graphite has played a central role in the generation of nuclear energy. That first reactor, dubbed the Chicago Pile, was constructed from about 40,000 graphite blocks, many of which contained nuggets of uranium.Today graphite is a vital component of many operating nuclear reactors and is expected to play a central role in next-generation reactor designs like molten-salt and high-temperature gas reactors. That’s because graphite is a good neutron moderator, slowing down the neutrons released by nuclear fission so they are more likely to create fissions themselves and sustain a chain reaction.“The simplicity of graphite makes it valuable,” Khaykovich explains. “It’s made of carbon, and it’s relatively well-known how to make it cleanly. Graphite is a very mature technology. It’s simple, stable, and we know it works.”But graphite also has its complexities.“We call graphite a composite even though it’s made up of only carbon atoms,” Khaykovich says. “It includes ‘filler particles’ that are more crystalline, then there is a matrix called a ‘binder’ that is less crystalline, then there are pores that span in length from nanometers to many microns.”Each graphite grade has its own composite structure, but they all contain fractals, or shapes that look the same at different scales.Those complexities have made it hard to predict how graphite will respond to radiation in microscopic detail, although it’s been known for decades that when graphite is irradiated, it first densifies, reducing its volume by up to 10 percent, before swelling and cracking. The volume fluctuation is caused by changes to graphite’s porosity and lattice stress.“Graphite deteriorates under radiation, as any material does,” Khaykovich says. “So, on the one hand we have a material that’s extremely well-known, and on the other hand, we have a material that is immensely complicated, with a behavior that’s impossible to predict through computer simulations.”For the study, the researchers received irradiated graphite samples from Oak Ridge National Laboratory. Co-authors Campbell and Snead were involved in irradiating the samples some 20 years ago. The samples are a grade of graphite known as G347A.The research team used an analysis technique known as X-ray scattering, which uses the scattered intensity of an X-ray beam to analyze the properties of material. Specifically, they looked at the distribution of sizes and surface areas of the sample’s pores, or what are known as the material’s fractal dimensions.“When you look at the scattering intensity, you see a large range of porosity,” Fayfar says. “Graphite has porosity over such large scales, and you have this fractal self-similarity: The pores in very small sizes look similar to pores spanning microns, so we used fractal models to relate different morphologies across length scales.”Fractal models had been used on graphite samples before, but not on irradiated samples to see how the material’s pore structures changed. The researchers found that when graphite is first exposed to radiation, its pores get filled as the material degrades.“But what was quite surprising to us is the [size distribution of the pores] turned back around,” Fayfar says. “We had this recovery process that matched our overall volume plots, which was quite odd. It seems like after graphite is irradiated for so long, it starts recovering. It’s sort of an annealing process where you create some new pores, then the pores smooth out and get slightly bigger. That was a big surprise.”The researchers found that the size distribution of the pores closely follows the volume change caused by radiation damage.“Finding a strong correlation between the [size distribution of pores] and the graphite’s volume changes is a new finding, and it helps connect to the failure of the material under irradiation,” Khaykovich says. “It’s important for people to know how graphite parts will fail when they are under stress and how failure probability changes under irradiation.”From research to reactorsThe researchers plan to study other graphite grades and explore further how pore sizes in irradiated graphite correlate with the probability of failure. They speculate that a statistical technique known as the Weibull Distribution could be used to predict graphite’s time until failure. The Weibull Distribution is already used to describe the probability of failure in ceramics and other porous materials like metal alloys.Khaykovich also speculated that the findings could contribute to our understanding of why materials densify and swell under irradiation.“There’s no quantitative model of densification that takes into account what’s happening at these tiny scales in graphite,” Khaykovich says. “Graphite irradiation densification reminds me of sand or sugar, where when you crush big pieces into smaller grains, they densify. For nuclear graphite, the crushing force is the energy that neutrons bring in, causing large pores to get filled with smaller, crushed pieces. But more energy and agitation create still more pores, and so graphite swells again. It’s not a perfect analogy, but I believe analogies bring progress for understanding these materials.”The researchers describe the paper as an important step toward informing graphite production and use in nuclear reactors of the future.“Graphite has been studied for a very long time, and we’ve developed a lot of strong intuitions about how it will respond in different environments, but when you’re building a nuclear reactor, details matter,” Khaykovich says. “People want numbers. They need to know how much thermal conductivity will change, how much cracking and volume change will happen. If components are changing volume, at some point you need to take that into account.”This work was supported, in part, by the U.S. Department of Energy. More

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      Theory-guided strategy expands the scope of measurable quantum interactions

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      A new theory-guided framework could help scientists probe the properties of new semiconductors for next-generation microelectronic devices, or discover materials that boost the performance of quantum computers.Research to develop new or better materials typically involves investigating properties that can be reliably measured with existing lab equipment, but this represents just a fraction of the properties that scientists could potentially probe in principle. Some properties remain effectively “invisible” because they are too difficult to capture directly with existing methods.Take electron-phonon interaction — this property plays a critical role in a material’s electrical, thermal, optical, and superconducting properties, but directly capturing it using existing techniques is notoriously challenging.Now, MIT researchers have proposed a theoretically justified approach that could turn this challenge into an opportunity. Their method reinterprets neutron scattering, an often-overlooked interference effect as a potential direct probe of electron-phonon coupling strength.The procedure creates two interaction effects in the material. The researchers show that, by deliberately designing their experiment to leverage the interference between the two interactions, they can capture the strength of a material’s electron-phonon interaction.The researchers’ theory-informed methodology could be used to shape the design of future experiments, opening the door to measuring new quantities that were previously out of reach.“Rather than discovering new spectroscopy techniques by pure accident, we can use theory to justify and inform the design of our experiments and our physical equipment,” says Mingda Li, the Class of 1947 Career Development Professor and an associate professor of nuclear science and engineering, and senior author of a paper on this experimental method.Li is joined on the paper by co-lead authors Chuliang Fu, an MIT postdoc; Phum Siriviboon and Artittaya Boonkird, both MIT graduate students; as well as others at MIT, the National Institute of Standards and Technology, the University of California at Riverside, Michigan State University, and Oak Ridge National Laboratory. The research appears this week in Materials Today Physics.Investigating interferenceNeutron scattering is a powerful measurement technique that involves aiming a beam of neutrons at a material and studying how the neutrons are scattered after they strike it. The method is ideal for measuring a material’s atomic structure and magnetic properties.When neutrons collide with the material sample, they interact with it through two different mechanisms, creating a nuclear interaction and a magnetic interaction. These interactions can interfere with each other.“The scientific community has known about this interference effect for a long time, but researchers tend to view it as a complication that can obscure measurement signals. So it hasn’t received much focused attention,” Fu says.The team and their collaborators took a conceptual “leap of faith” and decided to explore this oft-overlooked interference effect more deeply.They flipped the traditional materials research approach on its head by starting with a multifaceted theoretical analysis. They explored what happens inside a material when the nuclear interaction and magnetic interaction interfere with each other.Their analysis revealed that this interference pattern is directly proportional to the strength of the material’s electron-phonon interaction.“This makes the interference effect a probe we can use to detect this interaction,” explains Siriviboon.Electron-phonon interactions play a role in a wide range of material properties. They affect how heat flows through a material, impact a material’s ability to absorb and emit light, and can even lead to superconductivity.But the complexity of these interactions makes them hard to directly measure using existing experimental techniques. Instead, researchers often rely on less precise, indirect methods to capture electron-phonon interactions.However, leveraging this interference effect enables direct measurement of the electron-phonon interaction, a major advantage over other approaches.“Being able to directly measure the electron-phonon interaction opens the door to many new possibilities,” says Boonkird.Rethinking materials researchBased on their theoretical insights, the researchers designed an experimental setup to demonstrate their approach.Since the available equipment wasn’t powerful enough for this type of neutron scattering experiment, they were only able to capture a weak electron-phonon interaction signal — but the results were clear enough to support their theory.“These results justify the need for a new facility where the equipment might be 100 to 1,000 times more powerful, enabling scientists to clearly resolve the signal and measure the interaction,” adds Landry.With improved neutron scattering facilities, like those proposed for the upcoming Second Target Station at Oak Ridge National Laboratory, this experimental method could be an effective technique for measuring many crucial material properties.For instance, by helping scientists identify and harness better semiconductors, this approach could enable more energy-efficient appliances, faster wireless communication devices, and more reliable medical equipment like pacemakers and MRI scanners.   Ultimately, the team sees this work as a broader message about the need to rethink the materials research process.“Using theoretical insights to design experimental setups in advance can help us redefine the properties we can measure,” Fu says.To that end, the team and their collaborators are currently exploring other types of interactions they could leverage to investigate additional material properties.“This is a very interesting paper,” says Jon Taylor, director of the neutron scattering division at Oak Ridge National Laboratory, who was not involved with this research. “It would be interesting to have a neutron scattering method that is directly sensitive to charge lattice interactions or more generally electronic effects that were not just magnetic moments. It seems that such an effect is expectedly rather small, so facilities like STS could really help develop that fundamental understanding of the interaction and also leverage such effects routinely for research.”This work is funded, in part, by the U.S. Department of Energy and the National Science Foundation. More