Tag: MIT

  • MIT Spinout Ferveret Uses Nuclear-Inspired Cooling to Slash Data Center Energy and Water Use

    MIT Spinout Ferveret Uses Nuclear-Inspired Cooling to Slash Data Center Energy and Water Use

    Data centers are expanding rapidly to support the rise of artificial intelligence, and their energy consumption is projected to account for up to 17 percent of U.S. electricity by the end of the decade. Currently, about one-third of that power goes toward cooling the chips that run AI models. A new startup founded by two MIT researchers aims to change that by adapting a technique originally designed for nuclear reactors.

    Ferveret, co-founded by former MIT postdoc Reza Azizian and MIT Professor Matteo Bucci, has developed a liquid cooling system that uses no water and significantly less electricity than traditional air-based methods. The system submerges computer servers in a specialized fluid that absorbs heat far more efficiently than air. What sets it apart from other liquid cooling solutions is the size and behavior of the bubbles it generates: Ferveret’s Adaptive Phase Cooling (APC) technology produces much smaller bubbles that detach from the server surfaces more frequently, accelerating the heat transfer process.

    The company is already testing its technology with several major players, including CleanSpark, FuriosaAI, and Switch, one of the largest data center operators in the U.S. In collaboration with UCLA’s computer science department, Ferveret demonstrated that its APC solution improves computational power efficiency by 15 percent compared to state-of-the-art liquid cooling. When combined with the startup’s power control software, the system can boost the number of tokens generated per watt by up to 35 percent, according to the company.

    “Our goal is to make data centers as sustainable as possible and help them use every single watt of power to generate tokens, which are the most useful outputs,” says Azizian. “Our system enables the operation of more powerful chips, it helps data centers waste a lot less energy, and it accomplishes all that with zero water consumption.”

    The founders’ journey began during Azizian’s postdoc at MIT in 2013, where he worked with Bucci on heat transfer in nuclear reactors. After stints at Microsoft and Nvidia, Azizian realized the cooling inefficiencies in data centers firsthand. “I thought, ‘Holy crap, this is not how you cool facilities,’” he recalls. The pair founded Ferveret in 2021, applying decades of knowledge from nuclear engineering to optimize heat removal in computing environments.

    Ferveret’s system uses a liquid with a low boiling point and no toxic PFAS chemicals. The process, inspired by subcooled boiling in reactors, creates bubbles that quickly recondense, hastening the cooling cycle. The modular design—each server fits into a small box—makes deployment easier than traditional immersion tanks. The company also offers control software that dynamically adjusts power to each server for maximum efficiency.

    Beyond energy savings, the water-free cooling opens new possibilities for data center location. “The sun shines in places where you don’t have much water, so the advantage of us being water-free is we allow you to build data centers where you have solar energy but nothing to cool the data center down,” says Bucci. This could enable data centers in regions like Africa, the Middle East, and parts of America that lack water resources.

    Ferveret is part of Nvidia’s Inception program and is in talks with major cloud computing companies. The startup plans to announce expanded partnerships later this year as it scales its technology to support the growing AI industry without straining the planet.

  • Four MIT Affiliates Awarded 2026 Hertz Foundation Fellowships for Groundbreaking Research

    Four MIT Affiliates Awarded 2026 Hertz Foundation Fellowships for Groundbreaking Research

    The Hertz Foundation has announced the 2026 recipients of its prestigious fellowships in applied sciences, engineering, and mathematics, including four affiliates of the Massachusetts Institute of Technology (MIT). The award recognizes doctoral students who are pursuing innovative solutions to critical challenges in science and technology.

    The MIT-affiliated recipients are Annika Marschner, Alvin Q. Meng, Zachary S. Siegel, and Matthew Wanta. Each will receive five years of financial support, including a stipend and full tuition, providing them with the autonomy to pursue groundbreaking research in their graduate studies.

    “What particularly impresses me about this cohort is their fearlessness in taking on new challenges and advancing the frontiers of science,” said Philip Welkhoff, a Hertz Fellow and director of the malaria program at the Gates Foundation, who co-led the selection process. “Each has exhibited tremendous creativity, grit, and vision, and I cannot wait to see what each accomplishes with the freedom to innovate provided by the Hertz Fellowship.”

    In addition to funding, fellows gain lifelong access to Hertz Foundation programs, including events, mentoring, and networking opportunities. Since its establishment in 1963, the foundation has named over 1,300 fellows, whose connections have sparked collaborative startups, research, and commercialization across technology, science, and engineering fields. Hertz Fellows have contributed to breakthroughs in advanced medical therapies, global defense networks, and the James Webb Space Telescope.

    This year’s MIT-affiliated recipients are among 19 Hertz Foundation Fellows selected from across the United States.

    Annika Marschner

    Annika Marschner ’26 majored in mechanical engineering and will begin her PhD at MIT in the fall. Her undergraduate research focused on developing novel technologies for biointerfacing and bio-inspired systems, including a custom benchtop stereoscope-compatible incubator and extrusion-based desktop bioprinter for MIT’s Raman Lab, a light-based filamented bioprinting system for ETH Zürich’s Tissue Engineering and Biofabrication Lab, and large-scale hardware designs for robotic systems in MIT’s Biomimetic Robotics Lab. Her undergraduate thesis centered on improving the speed and dexterity of dynamic motions in bio-inspired robotic limbs. As a graduate student, she plans to continue her work on hardware and control system design in biologically relevant settings, particularly in assistive medical technology and surgical robotics.

    Alvin Q. Meng

    Alvin Q. Meng is a doctoral student in inorganic chemistry, focusing on understanding the fundamental interactions underlying chemical structure and reactivity. He is currently studying iron-sulfur clusters under the guidance of Professor Daniel L.M. Suess. Born in Tianjin, China, Meng immigrated to the United States at age 10. He earned undergraduate degrees in chemistry and mathematics from the University of Virginia, where he worked in the research group of Professor W. Dean Harman. His research involved the synthesis and characterization of dihapto-coordinated tungsten complexes of cyclopentadiene, focusing on a class of unusual binuclear species containing a carbon–carbon bond linking two metal-bound five-membered rings.

    Zachary S. Siegel

    Zachary S. Siegel is an electrical engineering and computer science graduate student pursuing a PhD in the Computer Science and Artificial Intelligence Laboratory (CSAIL), where he works at the intersection of robotics, cognitive science, and artificial intelligence. He graduated summa cum laude from Princeton University with a BSE in computer science and a minor in philosophy, receiving honors including Tau Beta Pi, Sigma Xi, and the Outstanding Computer Science Independent Work Prize. His senior thesis, advised by Tom Griffiths and Jacob Andreas, investigated how humans infer the goals of others in open-ended, real-world environments. Siegel demonstrated how Bayesian inference serves as an accurate model of people’s goal predictions by comparing partial observations to a learned library of possible plans weighted by their prior likelihood. His doctoral research aims to build machines that learn and reason more like people—systems that can learn from limited data and generalize to new situations by combining robot planning and Bayesian inference. He is particularly interested in combinatorial generalization: the human capacity to compose known skills in novel ways to solve previously unseen problems without additional demonstrations. At MIT, he is advised by Leslie P. Kaelbling, Tomás Lozano-Pérez, and Joshua B. Tenenbaum.

    Matthew Wanta

    Matthew Wanta is an incoming doctoral student who will begin operations research at MIT in the fall. He is a class of 2026 graduate of the United States Military Academy at West Point with a bachelor’s degree in computer science and mathematical sciences, both with honors. His work centered on machine learning for autonomous systems, integrating probabilistic modeling and computer vision into cooperative drone search and swarm control frameworks. In collaboration with DEVCOM Armaments Center, Wanta developed computer vision models for detecting energetic defects in artillery munitions, enabling rapid, nonintrusive quality control in defense manufacturing. His work with U.S. Special Operations Command and Army C5ISR organizations focused on autonomous aerial search and sensing, where he built simulation architectures for probabilistic target localization and multi-agent coordination. Wanta served as company commander for Bravo Company, 2nd Regiment; president of Upsilon Pi Epsilon; and vice president of Phi Kappa Phi. He is an Astronaut Scholar and Sapper School graduate, and commissioned as an Army officer in the Cyber Corps.

  • MIT’s New Manufacturing Initiative Gains Traction with Industry, Workforce, and Startup Programs

    MIT’s New Manufacturing Initiative Gains Traction with Industry, Workforce, and Startup Programs

    In its first year, the Initiative for New Manufacturing (INM) at MIT has built significant momentum by bridging research, workforce development, and industry engagement to accelerate the deployment of advanced manufacturing technologies. The initiative marked its first anniversary with MIT Manufacturing Week in May 2026, attracting over 800 registrants—including students, faculty, industry leaders, investors, entrepreneurs, and government officials—for four days of workshops, symposia, and discussions on AI in factories, startup innovation, and workforce solutions.

    “INM launched a year ago with the premise that strengthening the industrial base needed a coordinated response, and MIT has a responsibility to lead it,” said Paula T. Hammond, dean of MIT’s School of Engineering and co-chair of INM’s Steering Committee. “The response and participation level has been huge. MIT Manufacturing Week proved that the appetite for change—from students to chief executives—is real and urgent.”

    Manufacturing Week Highlights

    Manufacturing Week opened with a cybersecurity workshop co-led by INM and Google Cloud for industry members. It continued with the MIT MIMO (Machine Intelligence for Manufacturing Operations) symposium on deploying AI on factory floors, alongside sessions on workforce development, emerging technologies, startups, and industrial transformation. The week closed with a regional research showcase and competition featuring more than 140 graduate students and postdocs from across New England.

    Over the past year, INM also hosted a distinguished speaker series with manufacturing leaders including Keith Flynn (Anduril), Roland Busch (Siemens), and Venky Alagirisamy (Nike).

    Fostering New Manufacturing Startups

    A central goal of INM is to help students see manufacturing as a frontier for scientific discovery, technological innovation, entrepreneurship, and societal impact. The initiative partnered with NSF I-Corps New England to host its first manufacturing research showcase. More than 140 teams from 17 universities applied; 40 finalist teams received mentorship, and eight teams shared $50,000 in prize funding.

    The top prize for “most transformative innovation” went to MIT PhD student Jake Read for “The End of G Code,” a project on modular machine control architectures. Vatsal Patel (MIT) and Joshua Grace (Yale) won the research excellence category for “VisFT,” scalable six-axis force-torque sensors. Project themes included AI tools for manufacturing, semiconductor process control, robotics, digital twins, new materials, additive manufacturing, next-generation shipbuilding, and biomanufacturing.

    “Entrepreneurship is a transformative pathway to take research to market, and to drive faster innovation and scale-up,” said John Hart, INM faculty co-director and head of MIT’s Department of Mechanical Engineering. “At INM’s inaugural research showcase, we had tremendous interest from universities across New England, along with enthusiastic participation from industry, investors, and experienced founders. We are excited to build on this success and work toward a nationwide program and platform for entrepreneurship and translation in manufacturing.”

    Growing Industry Consortium

    During Manufacturing Week, First Solar became INM’s eighth industry member, joining Amgen, Autodesk, GE Vernova, Flex, PTC, Sanofi, and Siemens. The consortium’s growth reflects a recognition that modern manufacturing challenges—supply chain resilience, workforce development, industrial competitiveness—require cross-sector collaboration. INM provides a platform to convene and develop solutions through workshops and working groups on cybersecurity, digital twins, AI agents in regulatory environments, and continuous innovation.

    “Our members see MIT as a partner that can help them both address today’s challenges and think far into the future,” said Rick Locke, dean of the MIT Sloan School of Management and co-chair of INM’s steering committee. “This kind of multi-industry engagement is unusual and powerful.”

    Research and Workforce Development

    INM issued a call for proposals on AI and automation, receiving an overwhelming response and funding eight seed research projects. The initiative plans to publish eight white papers in June as part of a broader study on the future of manufacturing. During MIT’s Independent Activities Period in January 2026, INM collaborated with NSF I-Corps to guide 13 early-stage teams through customer discovery via the I-Corps Spark program.

    Workforce development is a major focus. MIT launched the Technologist Advanced Manufacturing Program (TechAMP), led by Principal Research Scientist John Liu, to create a new generation of shop floor leaders at six sites across New England, including three community colleges. “INM has the potential to transform the national manufacturing workforce,” said Liu. “It will require deep engagement between how people learn and lead, and how firms adopt new technologies and transform. We’re just getting started.” INM is now exploring a national rollout of TechAMP and expansion into biomanufacturing and semiconductor manufacturing.

    On campus, INM supported student engagements including an AI and automation lunch series and a Factory Observatory program. This spring, students founded MIT’s first manufacturing club, holding its launch event during Manufacturing Week. “We’re thrilled students are taking the lead,” said Sloan associate professor and INM faculty co-director Karen Zheng. “It was really exciting to see a full room of 80-plus students across campus coming together for the kickoff event during the busiest final period of a semester. This speaks to the students’ enthusiasm.”

    Global Reach and Future Plans

    While focused on strengthening domestic manufacturing, INM aims for global impact. It is collaborating with NAMTECH in Ahmedabad, India, where students are now taking an adaptation of MIT’s “yo-yo course” (2.008: Design and Manufacturing II). Next year, INM plans to bring more manufacturing leaders to campus, offer additional programming for emerging entrepreneurs, graduate TechAMP’s first cohort, expand TechAMP to new states, grow the consortium, and deepen research into manufacturing productivity.

    “INM aims to be a catalyst for transforming manufacturing across the nation to drive innovation, economic growth, and new types of jobs,” said Chris Love, faculty co-director of INM. “MIT’s work on the PIE (Production in the Innovation Economy) study in 2013 highlighted the value of proximity between production and innovation. INM seeks to rekindle this relationship in manufacturing across the country.”

  • MIT Develops Long-Term Memory System That Lets Robots Answer Where You Left Your Keys

    MIT Develops Long-Term Memory System That Lets Robots Answer Where You Left Your Keys

    Imagine asking a robot, “Where did I leave my keys?” and getting an accurate, real-time answer. MIT researchers have created a new spatial memory framework called DAAAM (Describe Anything, Anywhere, Anytime, at Any Moment) that gives robots the ability to form and recall detailed mental models of large-scale environments. This breakthrough could transform how robots assist humans in factories, homes, and beyond.

    DAAAM combines advanced map representations with rich, language-based descriptions of objects a robot encounters as it explores. The system runs fast enough for mobile robots to use in real-time, answering complex queries in plain English with 21% to 53% higher accuracy than existing methods.

    “If we want robots to work side-by-side with humans and interact better with humans, they must speak the same language,” says Luca Carlone, associate professor in MIT’s Department of Aeronautics and Astronautics and lead researcher on the project. “The robot must be able to reason about time and space the same way humans do.”

    The framework bridges computer vision and robotic mapping. As a robot moves through an environment, DAAAM attaches detailed descriptions to objects—like noting that a red bicycle with a flat tire is in the bike rack outside the Stata Center. It stores this information in a 3D map-based representation arranged spatially, grouping objects into regions for efficient retrieval.

    To overcome the speed limitations of existing annotation techniques, DAAAM aggregates nearby objects and uses an optimization method to select key frames—images with the clearest view of multiple objects—allowing the system to describe several items in parallel. This speeds up computation tenfold, making real-time performance possible.

    “We annotate every object only once, so our framework can run in very large-scale environments in real time,” explains lead author Nicolas Gorlo, an MIT graduate student. “And by clustering objects into regions, it can answer a wide range of queries about objects and locations.”

    The researchers used a large language model (LLM) that calls on various tools to retrieve specific information quickly, reducing hallucinations. For example, if asked about a sculpture near an MIT campus building, DAAAM can use a semantic search tool to retrieve information based on the word “sculpture” or a location-based tool to find the building.

    Future work aims to expand DAAAM to capture significant events and incorporate confidence levels into responses. “Ultimately, we want to have robots that can help with any sort of tasks,” Gorlo says. “With this framework, we are trying to create the foundations to enable a generalist agent that can do anything you ask.”

    The research was presented at the Conference on Computer Vision and Pattern Recognition (CVPR) and funded by the U.S. Army Research Laboratory and the Office of Naval Research.

  • MIT Leaders and Faculty Show How Massachusetts Can Drive the Next Wave of Tech Innovation

    MIT Leaders and Faculty Show How Massachusetts Can Drive the Next Wave of Tech Innovation

    On June 9, The Boston Globe released its 2026 “Tech Power Players” list, recognizing 50 influential local leaders in technology and business across Massachusetts. The list includes eight MIT affiliates: President Sally Kornbluth, Prof. Daniela Rus (director of CSAIL), Prof. Regina Barzilay, Prof. Yet-Ming Chiang, Prof. Max Tegmark, Ana Bakshi (executive director of the Martin Trust Center for MIT Entrepreneurship), Katie Rae (CEO and Managing Partner of Engine Ventures), and Senior Lecturer Brian Halligan, along with numerous MIT alumni.

    The Power Players coverage highlights MIT’s research labs, its culture of innovation and entrepreneurship, industry connections, new AI initiatives, and the Institute’s commitment to maintaining Massachusetts’ technological leadership. “Massachusetts can absolutely lead in this next wave,” says President Kornbluth, pointing to opportunities in manufacturing, life and health sciences, quantum technologies, and energy that can serve Americans nationwide.

    MIT is “working to drive artificial intelligence forward in sectors where the region is strongest, from biotechnology and robotics to defense and clean energy. It’s also trying to broaden entrepreneurship through a ‘dorm-to-startup’ push, creating a pipeline of support services—from hack-a-thons to venture funding—to help students start companies between classes,” writes Robert Weisman for The Globe. President Kornbluth is reinvigorating the school’s support of the local innovation ecosystem, unveiling new online AI classes—with free entry-level courses for anyone—and encouraging more on-campus entrepreneurship.

    MIT is carving out a specialty in applied AI, sometimes called “AI+X,” deploying the technology to help businesses, hospitals, and research institutions supercharge productivity, innovation, and scientific breakthroughs. Aman Narang ’04, CEO of Toast, notes: “The superpower has always been the university system. The best thing Boston can do is keep these people around.”

    MIT startups are a key driver of the region’s entrepreneurial ecosystem. To maintain Boston as a hub for innovators, MIT is building on its existing entrepreneurship resources, including more than 150 courses and 85 centers and programs. President Kornbluth and Provost Anantha Chandrakasan recently formed the Committee on Accelerating Translation and Entrepreneurship (CATE) to explore how the Institute can better support and accelerate the movement of ideas from research into new ventures.

    Applications for The Martin Trust Center’s startup accelerator program have doubled from last year, and nearly one-fifth of MIT undergraduates—about 800 students—attended a recent startup career fair.

    The Globe also highlights MIT startup Liquid AI, which develops AI models inspired by the brain structure of a simple worm. These models require far less electricity to operate than large language models, saving energy and water. Liquid AI recently signed a deal with Mercedes-Benz to incorporate its technology into onboard systems of cars sold in North America.

    Researchers are also focusing on energy innovation. Prof. Yet-Ming Chiang’s lab is developing batteries that store more electricity over longer periods, creating more opportunities for wind, solar, and other clean energy sources. Chiang’s lab and other MIT research centers work on microchips, critical minerals, fusion technology, and defense tech—examples of “tough tech” that combine science and engineering, which Chiang says “are in the sweet spot of the Boston ecosystem.”

    Soon, 80 MIT students will work as summer interns at GE Vernova, thanks to the MIT-GE Vernova Climate and Energy Alliance, a collaboration aimed at accelerating the global energy transition. GE Vernova CEO Scott Strazik dedicated $50 million over five years to fund internships and research projects.

    When asked about the most promising thing about the Greater Boston tech scene, Prof. Rus answers: “talent. Boston has the best AI researchers in the world, and they’re producing genuinely new ideas, not incremental ones.” Bob Mumgaard SM ’15, co-founder and CEO of Commonwealth Fusion Systems, says the region’s expertise in engineering, designing, and manufacturing hardware and access to university researchers made it possible to build the company only in Massachusetts. “The ecosystem has the building blocks… Massachusetts is the strongest in the nation in innovation in energy.”

    President Kornbluth points to quantum: “There isn’t a more important technological field right now than quantum science and technology, and the Boston area has the greatest concentration of quantum talent anywhere in the world.”

  • MIT Affiliates Shine in Boston Globe’s Tech Power Players List, Underscoring State’s Leadership Potential

    MIT Affiliates Shine in Boston Globe’s Tech Power Players List, Underscoring State’s Leadership Potential

    The Boston Globe released its 2026 “Tech Power Players” list on June 9, recognizing 50 influential leaders in technology and business across Massachusetts. Eight MIT affiliates are among the honorees, including President Sally Kornbluth, CSAIL Director Daniela Rus, Professors Regina Barzilay, Yet-Ming Chiang, and Max Tegmark, along with Ana Bakshi (Executive Director of the Martin Trust Center for MIT Entrepreneurship), Katie Rae (CEO of Engine Ventures), and Senior Lecturer Brian Halligan. The list also features numerous MIT alumni.

    The coverage goes beyond individual accolades, spotlighting MIT’s research labs, innovation culture, industry ties, new AI initiatives, and the Institute’s commitment to maintaining Massachusetts’ technological edge. “Massachusetts can absolutely lead in this next wave,” President Kornbluth told the Globe, emphasizing opportunities in manufacturing, life and health sciences, quantum technologies, and energy.

    AI and Entrepreneurship at the Forefront

    MIT is driving artificial intelligence forward in sectors where the region excels, from biotechnology and robotics to defense and clean energy. The Institute is also broadening entrepreneurship through a “dorm-to-startup” push, creating a pipeline of support services that include hackathons, venture funding, and more than 150 courses and 85 centers dedicated to fostering entrepreneurial ventures.

    President Kornbluth is reinvigorating MIT’s support for the local innovation ecosystem by unveiling new online AI classes—offering free entry-level courses for anyone—and encouraging more on-campus entrepreneurship. The Globe notes that these free courses could help local tech leaders ensure that people, not just corporations, benefit from AI.

    MIT is carving a specialty in applied AI, sometimes called “AI+X,” deploying the technology to help businesses, hospitals, and research institutions supercharge productivity and innovation. Aman Narang ’04, CEO of Toast, commented, “The superpower has always been the university system. The best thing Boston can do is keep these people around.”

    To further support student entrepreneurs, President Kornbluth and Provost Anantha Chandrakasan formed the Committee on Accelerating Translation and Entrepreneurship (CATE) to explore how MIT can accelerate the movement of ideas from research into new ventures. Applications for the Martin Trust Center’s startup accelerator program have doubled from last year, and nearly 800 undergraduates attended a recent startup career fair.

    Startups Driving Innovation Beyond Campus

    MIT startup Liquid AI is developing models inspired by the brain structure of a simple worm, which could significantly reduce AI energy consumption. The models, capable of detecting financial fraud and piloting autonomous drones, require far less electricity than large language models. Liquid AI recently signed a deal with Mercedes-Benz to integrate its technology into vehicles sold in North America.

    In energy, Professor Yet-Ming Chiang’s lab is developing long-duration batteries that enable greater use of wind, solar, and other clean energy sources. The lab and other MIT research centers are also working on innovations in microchips, critical minerals, fusion technology, and defense tech—examples of “tough tech” that Chiang says are in the sweet spot of the Boston ecosystem.

    Eighty MIT students will work as summer interns at GE Vernova through the MIT-GE Vernova Climate and Energy Alliance, a $50 million, five-year collaboration funding internships and research projects. GE Vernova CEO Scott Strazik emphasized the importance of plugging into MIT’s innovation culture.

    Quantum and the Future

    When asked about the most promising aspect of the Greater Boston tech scene, Daniela Rus pointed to talent: “Boston has the best AI researchers in the world, and they’re producing genuinely new ideas, not incremental ones.” Bob Mumgaard SM ’15, co-founder of Commonwealth Fusion Systems, noted that Massachusetts’ expertise in engineering and manufacturing made it the only place he could build his fusion energy company. “The ecosystem has the building blocks,” he said. “Massachusetts is the strongest in the nation in innovation in energy.”

    President Kornbluth highlighted quantum science and technology: “There isn’t a more important technological field right now than quantum science and technology, and the Boston area has the greatest concentration of quantum talent anywhere in the world.”

  • MIT Researchers Unveil New Method to Accurately Model Metal Alloy Behavior

    MIT Researchers Unveil New Method to Accurately Model Metal Alloy Behavior

    Companies working at the frontier of aerospace, energy, and computing are constantly searching for new materials to improve performance. However, understanding how these materials will behave inside rockets or on computer chips typically requires physically making and testing them first. This is because even the most powerful simulation techniques struggle to model the complex chemical arrangements found in most solid materials, adding significant costs and time to materials innovation.

    Now, a team of MIT researchers has developed a way to accurately model the behavior of metals, regardless of the complexity of their chemical arrangement. The core of the approach involves machine-learning models that accelerate and improve material simulations. The researchers enhanced these models by building training datasets that capture the diversity of atomic environments in chemically disordered materials.

    In a new paper published in Sciences Advances, the team demonstrated that their approach can accurately predict material properties for a diverse group of metal alloys under various conditions. They also showed how the method could be used to develop new materials, especially in scenarios where experimentation is expensive.

    “The focus of the paper is metallic alloys, but this could be adapted to other types of materials, like semiconductors,” says senior author Rodrigo Freitas, MIT’s TDK Career Development Professor in Materials Science and Engineering. “This is not specific to any one application — you could use this approach to create new sustainable steels, new materials for aerospace, and more. That’s what makes this exciting.”

    Modeling Metals

    Material properties are largely determined by the internal arrangement of their chemical elements. Even two materials with the same chemical composition can differ drastically — one might be brittle while the other deforms without breaking — depending on the atomic arrangement.

    Capturing that distinction requires simulating materials atom by atom. Researchers rely on models that describe how atoms interact. Over the last two decades, machine learning has become the most accurate way to build these models. Such models work well when chemical arrangements follow highly ordered patterns, but most solid materials are chemically disordered, with varying arrangements from one region to another.

    “The real challenge in our field is modelling these chemically disordered phases,” Freitas says. “Chemical disorder means there’s a huge variety of local chemical environments, which is hard for the machine-learning model to learn. This is a problem because every single metal we use in practice is chemically disordered.”

    The issue stems from a lack of representative training data for atom-by-atom simulations. The current leading approach uses brute force, often requiring over 100,000 hours of computation to create training data for a single material, and it does not transfer well when the material’s composition changes.

    Information Theory Approach

    In previous work, Freitas’ group developed a way to measure the chemical complexity of solid materials by analyzing the frequency and spacing of tiny groups of atoms. For this study, they used that capability to build better training datasets. They applied a mathematical approach known as information theory to generate datasets that capture a wider variety of local chemical environments inside disordered materials. The method works by swapping out atoms from samples to reduce repetition and expose the model to chemical environments it might otherwise miss.

    “We kept optimizing the training set so it captured as many different local environments as possible,” Freitas says. “If the same kind of environment showed up many times, we replaced redundant examples with ones the model hadn’t seen before. That makes the training set much more informative because each example adds something new.”

    When trained on the researchers’ datasets, the models predicted material properties more accurately than models trained using random sampling or another popular sampling method.

    “The starting point for all these atom-by-atom simulations is: Are you able to accurately describe the chemical bond between atoms?” Freitas explains. “If not, it can still teach you about materials in general, but it doesn’t tell you what will happen to specific materials in the real world. This approach makes the simulations high fidelity in terms of their chemistry, to better reflect what’s happening to materials.”

    The researchers applied their technique to create machine-learning training datasets for a group of chemically diverse metal alloys. Using a set of machine-learning models, they showed that models trained on their datasets are more accurate than much larger models created by companies like Google and Microsoft.

    From Lab to Industry

    The method works, in part, by capturing hidden patterns in the sample data — described as “subtle energetic biases toward certain local chemical configurations.” These small energetic differences determine which phases form in an alloy, how those phases change with temperature and composition, and ultimately what properties the material will have.

    As one test, simulations led by MIT PhD student Daniel Xiao showed that the team’s models could predict phase diagrams that closely matched experimental data. Phase diagrams map which phases are stable across different temperatures and chemical compositions, and they are a central tool for designing and processing alloys.

    “Phase diagrams are one of the main ways people connect materials modeling to real processing decisions,” Freitas says. “If you are welding, casting, or heat-treating an alloy, you need to know which phases are likely to form under different conditions. Our goal is to make these kinds of predictions accurate enough, and accessible enough, that they become part of how people design materials.”

    The researchers are now using the approach to study how changing an alloy’s composition affects mechanical properties and radiation tolerance, aiming to design materials that remain strong and damage-tolerant in harsh environments. They are also working to make the method easier to integrate with the tools and workflows materials engineers already rely on.

    “Industry isn’t going to change the way they do things if what you’re creating doesn’t fit into their existing operating procedures,” Freitas says. “The goal is to make these predictions useful in the places where materials decisions are actually made.”

    The research was supported by the U.S. Air Force Office of Scientific Research.

  • Ultra-Low-Power Chip Lets Tiny Robots Create Real-Time 3D Maps for Navigation

    Ultra-Low-Power Chip Lets Tiny Robots Create Real-Time 3D Maps for Navigation

    MIT researchers have unveiled a new chip that could transform how tiny, battery-powered robots navigate complex environments. The chip, named Gleanmer, enables small autonomous vehicles and other power-limited devices to build detailed 3D maps of their surroundings in real time while consuming only about 6 milliwatts of power — roughly the same as a single LED.

    Traditional 3D mapping systems require significant memory and energy because they store and process every pixel from depth images. The MIT team took a different approach, combining an efficient algorithm with custom hardware to dramatically reduce both power and memory demands.

    How Gleanmer Works

    Instead of representing the environment with rigid cube-shaped voxels (3D pixels), the chip uses flexible ellipsoid shapes called Gaussians. These ellipsoids can be smoothly adjusted in size, shape, and thickness to match curved objects more efficiently than voxels. This compact representation captures both obstacles and free space, allowing a robot to plan collision-free paths without storing massive amounts of data.

    The researchers leveraged an algorithm developed in their lab called GMMap, which generates Gaussian-based maps from depth images in a single pass. By only comparing each pixel to its immediate neighbors, the algorithm avoids storing entire images at once — significantly reducing memory footprint.

    “At any point in time, we only need to store a few pixels in memory, which significantly reduces the memory footprint our algorithm requires,” said co-lead author Peter Zhi Xuan Li, an MIT graduate student.

    Co-Design for Maximum Efficiency

    As a robot moves, it often sees the same object from multiple angles, creating overlapping Gaussians. The chip fuses these overlapping shapes directly — without revisiting the original pixels — keeping the map compact. This principle extends throughout the design: most computations operate on compact Gaussians rather than raw pixels, enabling major energy savings.

    The hardware is built to keep active Gaussians in small, fast on-chip memory right next to the processing units. This eliminates the need to fetch data from distant, power-hungry off-chip storage.

    “By having a dedicated memory that just stores the objects you’ve seen in the previous few frames, you can access the data much more efficiently,” explained co-lead author Zih-Sing Fu.

    Real-World Testing and Future Applications

    The team tested Gleanmer on diverse 3D environments and live data from an iPhone camera. The chip generated accurate maps in real time while using just 2.5% of the power required by the best existing map-construction chip. It also allowed a simulated robot to plan safe trajectories using only about 20% of the energy it would otherwise need.

    Beyond robotics, the ultra-low-power operation makes the chip well-suited for lightweight augmented reality headsets that can be worn for extended periods — for applications like medical training simulations or detailed repair work.

    “Real-time 3D mapping has been the missing piece for small autonomous systems. A drone inspecting a pipeline or a pair of AR glasses navigating a room both need to understand the space around them — instantly, continuously, and at almost no power cost. Gleanmer makes that possible for the first time in a chip you can hold between your fingers,” said co-author Sertac Karaman, a professor of aeronautics and astronautics.

    The work was supported by the MIT-MathWorks Fellowship, Amazon, the U.S. National Science Foundation, and Intel. The paper was presented at the IEEE Very Large-Scale Integrated Circuits Symposium.

  • MIT’s 6-Milliwatt AI Chip Brings Real-Time 3D Mapping to Tiny Robots

    MIT’s 6-Milliwatt AI Chip Brings Real-Time 3D Mapping to Tiny Robots

    Researchers at MIT have unveiled a new system-on-a-chip that could revolutionize how tiny, low-power robots navigate complex environments. The chip, named Gleanmer, enables autonomous drones and other battery-limited devices to construct detailed 3D maps in real time using only about 6 milliwatts of power—roughly the energy of a single LED. This breakthrough, presented at the IEEE Very Large-Scale Integrated Circuits Symposium, promises to make small-scale robotics more capable and energy-efficient than ever.

    A Smarter Approach to 3D Mapping

    Traditional 3D mapping for robots relies on power-hungry systems that process and store thousands of 3D pixels (voxels) per image. The MIT team took a radically different approach by combining an efficient algorithm called GMMap with custom hardware optimized for its workload. Instead of using rigid cube-shaped voxels, GMMap represents obstacles and free space with flexible ellipsoidal blobs known as Gaussians. These shapes can stretch and shrink to match curved objects, storing far less data than traditional methods while still capturing accurate geometry.

    “This paper showcases a key example of how you can leverage co-design of the algorithm and hardware to really push energy efficiency,” says Vivienne Sze, a professor in EECS and senior author of the paper. “Our chip allows you to store very large maps in a very small space, and do it in a very energy efficient manner.”

    Hardware-Algorithm Co-Design at Its Best

    The chip’s efficiency stems from several clever optimizations. First, the GMMap algorithm processes each depth image in a single pass, comparing only neighboring pixels rather than all pixels in an image. This drastically reduces the amount of data that needs to be stored and retrieved. Second, as the robot moves and observes the same object from different angles, overlapping Gaussians are fused into a single compact representation without re-examining the original pixels. Because Gaussians are more compact than raw pixel data, this fusion process runs on-chip with minimal memory traffic.

    The hardware design keeps active Gaussians in small, fast on-chip memory right next to the processing units. “By having a dedicated memory that just stores the objects you’ve seen in the previous few frames, you can access the data much more efficiently,” explains co-lead author Zih-Sing Fu.

    Unmatched Energy Efficiency

    In tests, Gleanmer generated detailed 3D maps in real time while consuming only about 6 milliwatts of power—just 2.5 percent of what the best existing mapping chip requires. When used for path planning, the chip reuses compact Gaussians along the planned route, using only about 20 percent of the energy that conventional approaches would need.

    The low power consumption makes Gleanmer ideal not only for tiny drones inspecting HVAC systems or pipelines, but also for lightweight augmented reality headsets that require continuous environmental mapping for educational medical simulations or repair work. Sertac Karaman, a professor of aeronautics and astronautics and co-author, notes: “Real-time 3D mapping has been the missing piece for small autonomous systems. Gleanmer makes that possible for the first time in a chip you can hold between your fingers.”

    Looking Ahead

    The researchers plan to push energy efficiency even further by moving processing units closer to the sensors that gather environmental data. They are also exploring applications beyond robotics, such as using Gaussians to represent complex schematics for AI reasoning systems. This work was supported by the MIT-MathWorks Fellowship, Amazon, the U.S. National Science Foundation, and Intel.

  • MIT Forum Explores How AI Reshapes Jobs, Democracy, and Society

    MIT Forum Explores How AI Reshapes Jobs, Democracy, and Society

    At the AI and Society Forum held at MIT, leading researchers from across the Institute gathered to examine the profound ways artificial intelligence is influencing employment, democratic processes, and the very fabric of society. Co-organized by the School of Humanities, Arts, and Social Sciences (SHASS) and the Social and Ethical Responsibilities of Computing (SERC), the event featured keynote talks, panel discussions, and even a musical performance blending generative AI with live instrumentation.

    AI and the Future of Work

    Economist David Autor challenged the widespread fear that AI will simply eliminate jobs. Instead, he argued that the technology’s impact hinges on how it alters the scarcity and value of human expertise. “When I think about how technology interacts with the value of labor, I think about it in terms of how it changes the scarcity of expertise, whether it makes it more valuable or whether it makes it more of a commodity,” Autor said. He emphasized that proactive policies—like worker training, wage insurance, and broader capital ownership—are essential to navigate the coming changes.

    During a panel moderated by Rob Loughlin of McKinsey & Company, MIT experts explored the changing nature of work. Daniela Rus, director of CSAIL, envisioned AI as a collaborative assistant: “I’d like to imagine the robot as your friend and assistant… but the role of the human as the decider, as the person with good judgment, remains super important.” David Mindell added that history shows work constantly evolves, and the key is to “constantly be creating the new work.” Sendhil Mullainathan cautioned that while AI offers productivity gains, long-term growth requires careful differentiation, and we are entering a period of high variance in workforce restructuring.

    Democracy and AI

    The second session turned to AI’s impact on democratic institutions. Chara Podimata of MIT Sloan presented research auditing large language models for bias in election information. Her study of 12 major models during the 2024 U.S. presidential election revealed that responses varied dramatically based on users’ stated demographics and political leanings. A new audit is planned for the 2026 midterms.

    In a panel moderated by Songyee Yoon, experts voiced both concerns and cautious optimism. Bailey Flanigan warned that automating decision-making could strip away the procedural rituals essential to democracy. Charles Stewart III highlighted the risk of AI-induced chaos in elections, noting that “if an election is called into question, that can lead to violence.” Lily Tsai argued that AI designers must embed democratic values such as agency, equality, and mutual respect. She shared a promising example: a Socratic dialogue chatbot that helps people articulate their beliefs, which actually moderated their policy positions.

    A Call for Interdisciplinary Action

    In his opening remarks, SHASS Dean Agustín Rayo stressed that “paying attention to the societal consequences of AI is not a departure from MIT’s mission; it’s a way of ensuring that our technical leadership has maximum impact.” Dan Huttenlocher, dean of the MIT Schwarzman College of Computing, echoed the need for interdisciplinary research to avoid overreliance and unintended consequences. The forum made clear that as AI continues to advance, understanding its societal impacts is as critical as the technology itself.