Transistors, the building blocks of modern electronics, are typically made of silicon. Because it’s a semiconductor, this material can control the flow of electricity in a circuit. But silicon has fundamental physical limits that restrict how compact and energy-efficient a transistor can be.

MIT researchers have now replaced silicon with a magnetic semiconductor, creating a magnetic transistor that could enable smaller, faster, and more energy-efficient circuits. The material’s magnetism strongly influences its electronic behavior, leading to more efficient control of the flow of electricity. 

The team used a novel magnetic material and an optimization process that reduces the material’s defects, which boosts the transistor’s performance.

The material’s unique magnetic properties also allow for transistors with built-in memory, which would simplify circuit design and unlock new applications for high-performance electronics.

“People have known about magnets for thousands of years, but there are very limited ways to incorporate magnetism into electronics. We have shown a new way to efficiently utilize magnetism that opens up a lot of possibilities for future applications and research,” says Chung-Tao Chou, an MIT graduate student in the departments of Electrical Engineering and Computer Science (EECS) and Physics, and co-lead author of a paper on this advance.

Chou is joined on the paper by co-lead author Eugene Park, a graduate student in the Department of Materials Science and Engineering (DMSE); Julian Klein, a DMSE research scientist; Josep Ingla-Aynes, a postdoc in the MIT Plasma Science and Fusion Center; Jagadeesh S. Moodera, a senior research scientist in the Department of Physics; and senior authors Frances Ross, TDK Professor in DMSE; and Luqiao Liu, an associate professor in EECS, and a member of the Research Laboratory of Electronics; as well as others at the University of Chemistry and Technology in Prague. The paper appears today in Physical Review Letters.

Overcoming the limits

In an electronic device, silicon semiconductor transistors act like tiny light switches that turn a circuit on and off, or amplify weak signals in a communication system. They do this using a small input voltage.

But a fundamental physical limit of silicon semiconductors prevents a transistor from operating below a certain voltage, which hinders its energy efficiency.

To make more efficient electronics, researchers have spent decades working toward magnetic transistors that utilize electron spin to control the flow of electricity. Electron spin is a fundamental property that enables electrons to behave like tiny magnets.

So far, scientists have mostly been limited to using certain magnetic materials. These lack the favorable electronic properties of semiconductors, constraining device performance.

“In this work, we combine magnetism and semiconductor physics to realize useful spintronic devices,” Liu says.

The researchers replace the silicon in the surface layer of a transistor with chromium sulfur bromide, a two-dimensional material that acts as a magnetic semiconductor.

Due to the material’s structure, researchers can switch between two magnetic states very cleanly. This makes it ideal for use in a transistor that smoothly switches between “on” and “off.”

“One of the biggest challenges we faced was finding the right material. We tried many other materials that didn’t work,” Chou says.

They discovered that changing these magnetic states modifies the material’s electronic properties, enabling low-energy operation. And unlike many other 2D materials, chromium sulfur bromide remains stable in air.

To make a transistor, the researchers pattern electrodes onto a silicon substrate, then carefully align and transfer the 2D material on top. They use tape to pick up a tiny piece of material, only a few tens of nanometers thick, and place it onto the substrate.

“A lot of researchers will use solvents or glue to do the transfer, but transistors require a very clean surface. We eliminate all those risks by simplifying this step,” Chou says.

Leveraging magnetism

This lack of contamination enables their device to outperform existing magnetic transistors. Most others can only create a weak magnetic effect, changing the flow of current by a few percent or less. Their new transistor can switch or amplify the electric current by a factor of 10.

They use an external magnetic field to change the magnetic state of the material, switching the transistor using significantly less energy than would usually be required.

The material also allows them to control the magnetic states with electric current. This is important because engineers cannot apply magnetic fields to individual transistors in an electronic device. They need to control each one electrically.

The material’s magnetic properties could also enable transistors with built-in memory, simplifying the design of logic or memory circuits.

A typical memory device has a magnetic cell to store information and a transistor to read it out. Their method can combine both into one magnetic transistor.

“Now, not only are transistors turning on and off, they are also remembering information. And because we can switch the transistor with greater magnitude, the signal is much stronger so we can read out the information faster, and in a much more reliable way,” Liu says.

Building on this demonstration, the researchers plan to further study the use of electrical current to control the device. They are also working to make their method scalable so they can fabricate arrays of transistors.

This research was supported, in part, by the Semiconductor Research Corporation, the U.S. Defense Advanced Research Projects Agency (DARPA), the U.S. National Science Foundation (NSF), the U.S. Department of Energy, the U.S. Army Research Office, and the Czech Ministry of Education, Youth, and Sports. The work was partially carried out at the MIT.nano facilities.

This article may be updated.

Nuno Loureiro, a professor of nuclear science and engineering and of physics at MIT, has died. He was 47.

A lauded theoretical physicist and fusion scientist, and director of the MIT Plasma Science and Fusion Center, Loureiro joined MIT’s faculty in 2016. His research addressed complex problems lurking at the center of fusion vacuum chambers and at the edges of the universe.

Loureiro’s research at MIT advanced scientists’ understanding of plasma behavior, including turbulence, and uncovered the physics behind astronomical phenomena like solar flares. He was the Herman Feshbach (1942) Professor of Physics at MIT and was named director of the Plasma Science and Fusion Center in 2024, though his contributions to fusion science and engineering began far before that.

His research on magnetized plasma dynamics, magnetic field amplification, and confinement and transport in fusion plasmas helped inform the design of fusion devices that could harness the energy of fusing plasmas, bringing the dream of clean, near-limitless fusion power closer to reality.

“Nuno was not only a brilliant scientist, he was a brilliant person,” says Dennis Whyte, the Hitachi America Professor of Engineering, who previously served as the head of the Department of Nuclear Science and Engineering and director of the Plasma Science and Fusion Center. “He shone a bright light as a mentor, friend, teacher, colleague and leader, and was universally admired for his articulate, compassionate manner. His loss is immeasurable to our community at the PSFC, NSE and MIT, and around the entire fusion and plasma research world.”

“Nuno was a champion for plasma physics within the Physics Department, a wonderful and engaging colleague, and an inspiring and caring mentor for graduate students working in plasma science.  His recent work on quantum computing algorithms for plasma physics simulations was a particularly exciting new scientific direction,” says Deepto Chakrabarty, the William A. M. Burden Professor in Astrophysics and head of the Department of Physics.

Whether working on fusion or astrophysics research, Loureiro merged fundamental physics with technology and engineering, to maximize impact.

“There are people who are driven by technology and engineering, and others who are driven by fundamental mathematics and physics. We need both,” Loureiro said in 2019. “When we stimulate theoretically inclined minds by framing plasma physics and fusion challenges as beautiful theoretical physics problems, we bring into the game incredibly brilliant students — people who we want to attract to fusion development.”

Loureiro majored in physics at Instituto Superior Tecnico (IST) in Portugal and obtained a PhD in physics at Imperial College London in 2005. He conducted postdoctoral work at the Princeton Plasma Physics Laboratory for the next two years before moving to the UKAEA Culham Center for Fusion Energy in 2007. Loureiro returned to IST in 2009, where he was a researcher at the Institute for Plasmas and Nuclear Fusion until coming to MIT in 2016.

He wasted no time contributing to the intellectual environment at MIT, spending part of his first two years at the Institute working on the vexing problem of plasma turbulence. Plasma is the super-hot state of matter that serves as the fuel for fusion reactors. Loureiro’s lab at PSFC illuminated how plasma behaves inside fusion reactors, which could help prevent material failures and better contain the plasma to harvest electricity.

“Nuno was not only an extraordinary scientist and educator, but also a tremendous colleague, mentor, and friend who cared deeply about his students and his community. His absence will be felt profoundly across NSE and far beyond,” Benoit Forget, the KEPCO Professor and head of the Department of Nuclear Science and Engineering, wrote in an email to the department today.

On other fronts, Loureiro’s work in astrophysics helped reveal fundamental mechanisms of the universe. He put forward the first theory of turbulence in pair plasmas, which differ from regular plasmas and may be abundant in space. The work was driven, in part, by unprecedented observations of a binary neutron star merger in 2018.

As an assistant professor and then a full professor at MIT, Loureiro taught course 22.612 (Intro to Plasma Physics) and course 22.615 (MHD Theory of Fusion Systems), for which he was twice recognized with the Department of Nuclear Science and Engineering’s PAI Outstanding Professor Award.

Loureiro’s research earned him many prominent awards throughout his prolific career, including the National Science Foundation Career Award and the American Physical Society Thomas H. Stix Award for Outstanding Early Career Contributions to Plasma Physics Research. He was also an APS fellow. Earlier this year, he earned the Presidential Early Career Award for Scientists and Engineers.

The world’s most common construction material has a secret. Cement, the “glue” that holds concrete together, gradually “breathes in” and stores millions of tons of carbon dioxide (CO2) from the air over the lifetimes of buildings and infrastructure.  

A new study from the MIT Concrete Sustainability Hub quantifies this process, carbon uptake, at a national scale for the first time. Using a novel approach, the research team found that the cement in U.S. buildings and infrastructure sequesters over 6.5 million metric tons of CO2 annually. This corresponds to roughly 13 percent of the process emissions — the CO2 released by the underlying chemical reaction — in U.S. cement manufacturing. In Mexico, the same building stock sequesters about 5 million tons a year.   

But how did the team come up with those numbers? 

Scientists have known how carbon uptake works for decades. CO2 enters concrete or mortar — the mixture that glues together blocks, brick, and stones — through tiny pores, reacts with the calcium-rich products in cement, and becomes locked into a stable mineral called calcium carbonate, or limestone. 

The chemistry is well-known, but calculating the magnitude of this at scale is not. A concrete highway in Dallas sequesters CO2 differently than Mexico City apartments made from concrete masonry units (CMUs), also called concrete blocks or, colloquially, cinder blocks. And a foundation slab buried under the snow in Fairbanks, Alaska, “breathes in” CO2 at a different pace entirely. 

As Hessam AzariJafari, lead author and research scientist in the MIT Department of Civil and Environmental Engineering, explains, “Carbon uptake is very sensitive to context. Four major factors drive it: the type of cement used, the product we make with it — concrete, CMUs, or mortar — the geometry of the structure, and the climate and conditions it’s exposed to. Even within the same structure, uptake can vary five-fold between different elements.” 

As no two structures sequester CO2 in the same way, estimating uptake nationwide would normally require simulating an array of cement-based elements: slabs, walls, beams, columns, pavements, and more. On top of that, each of those has its own age, geometry, mixture, and exposure condition to account for.  

Seeing that this approach would be like trying to count every grain of sand on a beach, the team took a different route. They developed hundreds of archetypes, typical designs that could stand in for different buildings and pieces of infrastructure. It’s a bit like measuring the beach instead by mapping out its shape, depth, and shoreline to estimate how much sand usually sits in a given spot.  

With these archetypes in hand, the team modeled how each one sequesters CO2 in different environments and how common each is across every state in the United States and Mexico. In this way, they could estimate not just how much CO2 structures sequester, but why those numbers differ.  

Two factors stood out. The first was the “construction trend,” or how the amount of new construction had changed over the previous five years. Because it reflects how quickly cement products are being added to the building stock, it shapes how much cement each state consumes and, therefore, how much of that cement is actively carbonating. The second was the ratio of mortar to concrete, since porous mortars sequester CO2 an order of magnitude faster than denser concrete. 

In states where mortar use was higher, the fraction of CO2 uptake relative to process emissions was noticeably greater. “We observed something unique about Mexico: Despite using half the cement that the U.S. does, the country has three-quarters of the uptake,” notes AzariJafari. “This is because Mexico makes more use of mortars and lower-strength concrete, and bagged cement mixed on-site. These practices are why their uptake sequesters about a quarter of their cement manufacturing emissions.” 

While care must be taken for structural elements that use steel reinforcement, as uptake can accelerate corrosion, it’s possible to enhance the uptake of many elements without negative impacts. 

Randolph Kirchain, director of the MIT Concrete Sustainability Hub, principal research scientist in the MIT Materials Research Laboratory, and the senior author of this study, explains: “For instance, increasing the amount of surface area exposed to air accelerates uptake and can be achieved by foregoing painting or tiling, or choosing designs like waffle slabs with a higher surface area-to-volume ratio. Additionally, avoiding unnecessarily stronger, less-porous concrete mixtures than required would speed up uptake while using less cement.” 

“There is a real opportunity to refine how carbon uptake from cement is represented in national inventories,” AzariJafari comments. “The buildings around us and the concrete beneath our feet are constantly ‘breathing in’ millions of tons of CO2. Nevertheless, some of the simplified values in widely used reporting frameworks can lead to higher estimates than what we observe empirically. Integrating updated science into international inventories and guidelines such as the Intergovernmental Panel on Climate Change (IPCC) would help ensure that reported numbers reflect the material and temporal realities of the sector.” 

By offering the first rigorous, bottom-up estimation of carbon uptake at a national scale, the team’s work provides a more representative picture of cement’s environmental impact. As we work to decarbonize the built environment, understanding what our structures are already doing in the background may be just as important as the innovations we pursue moving forward. The approach developed by MIT researchers could be extended to other countries by combining global building-stock databases with national cement-production statistics. It could also inform the design of structures that safely maximize uptake. 

The findings were published Dec. 15 in the  Proceedings of the National Academy of Sciences. Joining AzariJafari and Kirchain on the paper are MIT researchers Elizabeth Moore of the Department of Materials Science and Engineering and the MIT Climate Project and former postdocs Ipek Bensu Manav SM ’21, PhD ’24 and Motahareh Rahimi, along with Bruno Huet and Christophe Levy from the Holcim Innovation Center in France.

The final EFFector of 2025 is here! Just in time to keep you up-to-date on the latests happenings in the fight for privacy and free speech online.

In this latest issue, we're sharing how to spot sneaky ALPR cameras at the U.S. border, covering a host of new resources on age verification laws, and explaining why AI companies need to protect chatbot logs from bulk surveillance.

Prefer to listen in? Check out our audio companion, where EFF Activist Molly Buckley explains our new resource explaining age verification laws and how you can fight back. Catch the conversation on YouTube or the Internet Archive.

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EFFECTOR 37.18 - 🪪 AGE VERIFICATION IS COMING FOR THE INTERNET

Since 1990 EFF has published EFFector to help keep readers on the bleeding edge of their digital rights. We know that the intersection of technology, civil liberties, human rights, and the law can be complicated, so EFFector is a great way to stay on top of things. The newsletter is chock full of links to updates, announcements, blog posts, and other stories to help keep readers—and listeners—up to date on the movement to protect online privacy and free expression. 

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New report: “The Party’s AI: How China’s New AI Systems are Reshaping Human Rights.” From a summary article:

China is already the world’s largest exporter of AI powered surveillance technology; new surveillance technologies and platforms developed in China are also not likely to simply stay there. By exposing the full scope of China’s AI driven control apparatus, this report presents clear, evidence based insights for policymakers, civil society, the media and technology companies seeking to counter the rise of AI enabled repression and human rights violations, and China’s growing efforts to project that repression beyond its borders...

The House-passed National Defense Authorization Act would spur an investigation of whether the U.S. military bio-engineered Lyme disease.

Withdrawals are increasing as the government hikes its premiums to in part compensate for damage associated with climate change.

The price of crude has dropped since the president ordered the capture of a sanctioned tanker carrying 2 million barrels of Venezuelan oil.

The Energy secretary said supporting the use of more fossil fuels is “common sense" as he bashed renewables.

The price of Washington's pollution allowances hit a record high. The state might soften its ambitions deadlines for emissions reductions.

The bill would require certain fossil fuel companies to contribute to a state fund to compensate for their impact on climate change.

The administration is favoring oil giants in a fight over Louisiana's disappearing coastline.

Opponents of the EU’s 2035 combustion engine ban are proclaiming victory, but there’s a long way to go before the law is changed.

It is developing vehicles tailored to Chinese drivers — cars that will likely never be seen on EU roads.

Reaching the climate targets and schedule “would have required significant investments that are currently not realistic,” the company said.

North Africa has been plagued by years of drought, hardening soils and making mountains, deserts and plains more susceptible to flooding.

Researchers at MIT and Stanford University have developed a new way to stimulate the immune system to attack tumor cells, using a strategy that could make cancer immunotherapy work for many more patients.

The key to their approach is reversing a “brake” that cancer cells engage to prevent immune cells from launching an attack. This brake is controlled by sugar molecules known as glycans that are found on the surface of cancer cells.

By blocking those glycans with molecules called lectins, the researchers showed they could dramatically boost the immune system’s response to cancer cells. To achieve this, they created multifunctional molecules known as AbLecs, which combine a lectin with a tumor-targeting antibody.

“We created a new kind of protein therapeutic that can block glycan-based immune checkpoints and boost anti-cancer immune responses,” says Jessica Stark, the Underwood-Prescott Career Development Professor in the MIT departments of Biological Engineering and Chemical Engineering. “Because glycans are known to restrain the immune response to cancer in multiple tumor types, we suspect our molecules could offer new and potentially more effective treatment options for many cancer patients.”

Stark, who is also a member of MIT’s Koch Institute for Integrative Cancer Research, is the lead author of the paper. Carolyn Bertozzi, a professor of chemistry at Stanford and director of the Sarafan ChEM Institute, is the senior author of the study, which appears today in Nature Biotechnology.

Releasing the brakes

Training the immune system to recognize and destroy tumor cells is a promising approach to treating many types of cancer. One class of immunotherapy drugs known as checkpoint inhibitors stimulate immune cells by blocking an interaction between the proteins PD-1 and PD-L1. This removes a brake that tumor cells use to prevent immune cells like T cells from killing cancer cells.

Drugs targeting the PD-1- PD-L1 checkpoint have been approved to treat several kinds of cancer. In some of these patients, checkpoint inhibitors can lead to long-lasting remission, but for many others, they don’t work at all.

In hopes of generating immune responses in a greater number of patients, researchers are now working on ways to target other immunosuppressive interactions between cancer cells and immune cells. One such interaction occurs between glycans on tumor cells and receptors found on immune cells.

Glycans are found on nearly all living cells, but tumor cells often express glycans that are not found on healthy cells, including glycans that contain a monosaccharide called sialic acid. When sialic acids bind to lectin receptors, located on immune cells, it turns on an immunosuppressive pathway in the immune cells. These lectins that bind to sialic acid are known as Siglecs.

“When Siglecs on immune cells bind to sialic acids on cancer cells, it puts the brakes on the immune response. It prevents that immune cell from becoming activated to attack and destroy the cancer cell, just like what happens when PD-1 binds to PD-L1,” Stark says.

Currently, there aren’t any approved therapies that target this Siglec-sialic acid interaction, despite a number of drug development approaches that have been tried. For example, researchers have tried to develop lectins that could bind to sialic acids and prevent them from interacting with immune cells, but so far, this approach hasn’t worked well because lectins don’t bind strongly enough to accumulate on the cancer cell surface in large numbers.

To overcome that, Stark and her colleagues developed a way to deliver larger quantities of lectins by attaching them to antibodies that target cancer cells. Once there, the lectins can bind to sialic acid, preventing sialic acid from interacting with Siglec receptors on immune cells. This lifts the brakes off the immune response, allowing immune cells such as macrophages and natural killer (NK) cells to launch an attack on the tumor.

“This lectin binding domain typically has relatively low affinity, so you can’t use it by itself as a therapeutic. But, when the lectin domain is linked to a high-affinity antibody, you can get it to the cancer cell surface where it can bind and block sialic acids,” Stark says.

A modular system

In this study, the researchers designed an AbLec based on the antibody trastuzumab, which binds to HER2 and is approved as a cancer therapy to treat breast, stomach, and colorectal cancers. To form the AbLec, they replaced one arm of the antibody with a lectin, either Siglec-7 or Siglec-9.

Tests using cells grown in the lab showed that this AbLec rewired immune cells to attack and destroy cancer cells.

The researchers then tested their AbLecs in a mouse model that was engineered to express human Siglec receptors and antibody receptors. These mice were then injected with cancer cells that formed metastases in the lungs. When treated with the AbLec, these mice showed fewer lung metastases than mice treated with trastuzumab alone.

The researchers also showed that they could swap in other tumor-specific antibodies, such as rituximab, which targets CD20, or cetuximab, which targets EGFR. They could also swap in lectins that target other glycans involved in immunosuppression, or antibodies that target checkpoint proteins such as PD-1.

“AbLecs are really plug-and-play. They’re modular,” Stark says. “You can imagine swapping out different decoy receptor domains to target different members of the lectin receptor family, and you can also swap out the antibody arm. This is important because different cancer types express different antigens, which you can address by changing the antibody target.”

Stark, Bertozzi, and others have started a company called Valora Therapeutics, which is now working on developing lead AbLec candidates. They hope to begin clinical trials in the next two to three years.

The research was funded, in part, by a Burroughs Wellcome Fund Career Award at the Scientific Interface, a Society for Immunotherapy of Cancer Steven A. Rosenberg Scholar Award, a V Foundation V Scholar Grant, the National Cancer Institute, the National Institute of General Medical Sciences, a Merck Discovery Biologics SEEDS grant, an American Cancer Society Postdoctoral Fellowship, and a Sarafan ChEM-H Postdocs at the Interface seed grant.

Computer-aided design (CAD) systems are tried-and-true tools used to design many of the physical objects we use each day. But CAD software requires extensive expertise to master, and many tools incorporate such a high level of detail they don’t lend themselves to brainstorming or rapid prototyping.

In an effort to make design faster and more accessible for non-experts, researchers from MIT and elsewhere developed an AI-driven robotic assembly system that allows people to build physical objects by simply describing them in words.

Their system uses a generative AI model to build a 3D representation of an object’s geometry based on the user’s prompt. Then, a second generative AI model reasons about the desired object and figures out where different components should go, according to the object’s function and geometry.

The system can automatically build the object from a set of prefabricated parts using robotic assembly. It can also iterate on the design based on feedback from the user.

The researchers used this end-to-end system to fabricate furniture, including chairs and shelves, from two types of premade components. The components can be disassembled and reassembled at will, reducing the amount of waste generated through the fabrication process.

They evaluated these designs through a user study and found that more than 90 percent of participants preferred the objects made by their AI-driven system, as compared to different approaches.

While this work is an initial demonstration, the framework could be especially useful for rapid prototyping complex objects like aerospace components and architectural objects. In the longer term, it could be used in homes to fabricate furniture or other objects locally, without the need to have bulky products shipped from a central facility.

“Sooner or later, we want to be able to communicate and talk to a robot and AI system the same way we talk to each other to make things together. Our system is a first step toward enabling that future,” says lead author Alex Kyaw, a graduate student in the MIT departments of Electrical Engineering and Computer Science (EECS) and Architecture.

Kyaw is joined on the paper by Richa Gupta, an MIT architecture graduate student; Faez Ahmed, associate professor of mechanical engineering; Lawrence Sass, professor and chair of the Computation Group in the Department of Architecture; senior author Randall Davis, an EECS professor and member of the Computer Science and Artificial Intelligence Laboratory (CSAIL); as well as others at Google Deepmind and Autodesk Research. The paper was recently presented at the Conference on Neural Information Processing Systems.

Generating a multicomponent design

While generative AI models are good at generating 3D representations, known as meshes,  from text prompts, most do not produce uniform representations of an object’s geometry that have the component-level details needed for robotic assembly.

Separating these meshes into components is challenging for a model because assigning components depends on the geometry and functionality of the object and its parts.

The researchers tackled these challenges using a vision-language model (VLM), a powerful generative AI model that has been pre-trained to understand images and text. They task the VLM with figuring out how two types of prefabricated parts, structural components and panel components, should fit together to form an object.

“There are many ways we can put panels on a physical object, but the robot needs to see the geometry and reason over that geometry to make a decision about it. By serving as both the eyes and brain of the robot, the VLM enables the robot to do this,” Kyaw says.

A user prompts the system with text, perhaps by typing “make me a chair,” and gives it an AI-generated image of a chair to start.

Then, the VLM reasons about the chair and determines where panel components go on top of structural components, based on the functionality of many example objects it has seen before. For instance, the model can determine that the seat and backrest should have panels to have surfaces for someone sitting and leaning on the chair.

It outputs this information as text, such as “seat” or “backrest.” Each surface of the chair is then labeled with numbers, and the information is fed back to the VLM.

Then the VLM chooses the labels that correspond to the geometric parts of the chair that should receive panels on the 3D mesh to complete the design.

Human-AI co-design

The user remains in the loop throughout this process and can refine the design by giving the model a new prompt, such as “only use panels on the backrest, not the seat.”

“The design space is very big, so we narrow it down through user feedback. We believe this is the best way to do it because people have different preferences, and building an idealized model for everyone would be impossible,” Kyaw says.

“The human‑in‑the‑loop process allows the users to steer the AI‑generated designs and have a sense of ownership in the final result,” adds Gupta.

Once the 3D mesh is finalized, a robotic assembly system builds the object using prefabricated parts. These reusable parts can be disassembled and reassembled into different configurations.

The researchers compared the results of their method with an algorithm that places panels on all horizontal surfaces that are facing up, and an algorithm that places panels randomly. In a user study, more than 90 percent of individuals preferred the designs made by their system.

They also asked the VLM to explain why it chose to put panels in those areas.

“We learned that the vision language model is able to understand some degree of the functional aspects of a chair, like leaning and sitting, to understand why it is placing panels on the seat and backrest. It isn’t just randomly spitting out these assignments,” Kyaw says.

In the future, the researchers want to enhance their system to handle more complex and nuanced user prompts, such as a table made out of glass and metal. In addition, they want to incorporate additional prefabricated components, such as gears, hinges, or other moving parts, so objects could have more functionality.

“Our hope is to drastically lower the barrier of access to design tools. We have shown that we can use generative AI and robotics to turn ideas into physical objects in a fast, accessible, and sustainable manner,” says Davis.

Nature Climate Change, Published online: 16 December 2025; doi:10.1038/s41558-025-02523-7

Climate denial in political discourse is fuelled by psychological factors such as psychological distance, cognitive dissonance, confirmation bias, loss aversion, existential anxiety and social identity. Effective communication strategies addressing deniers’ motivations are crucial as denial undermines urgent climate action.

Today, out of an estimated 1 trillion species on Earth, 99.999 percent are considered microbial — bacteria, archaea, viruses, and single-celled eukaryotes. For much of our planet’s history, microbes ruled the Earth, able to live and thrive in the most extreme of environments. Researchers have only just begun in the last few decades to contend with the diversity of microbes — it’s estimated that less than 1 percent of known genes have laboratory-validated functions. Computational approaches offer researchers the opportunity to strategically parse this truly astounding amount of information.

An environmental microbiologist and computer scientist by training, new MIT faculty member Yunha Hwang is interested in the novel biology revealed by the most diverse and prolific life form on Earth. In a shared faculty position as the Samuel A. Goldblith Career Development Professor in the Department of Biology, as well as an assistant professor at the Department of Electrical Engineering and Computer Science and the MIT Schwarzman College of Computing, Hwang is exploring the intersection of computation and biology.  

Q: What drew you to research microbes in extreme environments, and what are the challenges in studying them? 

A: Extreme environments are great places to look for interesting biology. I wanted to be an astronaut growing up, and the closest thing to astrobiology is examining extreme environments on Earth. And the only thing that lives in those extreme environments are microbes. During a sampling expedition that I took part in off the coast of Mexico, we discovered a colorful microbial mat about 2 kilometers underwater that flourished because the bacteria breathed sulfur instead of oxygen — but none of the microbes I was hoping to study would grow in the lab. 

The biggest challenge in studying microbes is that a majority of them cannot be cultivated, which means that the only way to study their biology is through a method called metagenomics. My latest work is genomic language modeling. We’re hoping to develop a computational system so we can probe the organism as much as possible “in silico,” just using sequence data. A genomic language model is technically a large language model, except the language is DNA as opposed to human language. It’s trained in a similar way, just in biological language as opposed to English or French. If our objective is to learn the language of biology, we should leverage the diversity of microbial genomes. Even though we have a lot of data, and even as more samples become available, we’ve just scratched the surface of microbial diversity. 

Q: Given how diverse microbes are and how little we understand about them, how can studying microbes in silico, using genomic language modeling, advance our understanding of the microbial genome? 

A: A genome is many millions of letters. A human cannot possibly look at that and make sense of it. We can program a machine, though, to segment data into pieces that are useful. That’s sort of how bioinformatics works with a single genome. But if you’re looking at a gram of soil, which can contain thousands of unique genomes, that’s just too much data to work with — a human and a computer together are necessary in order to grapple with that data. 

During my PhD and master’s degree, we were only just discovering new genomes and new lineages that were so different from anything that had been characterized or grown in the lab. These were things that we just called “microbial dark matter.” When there are a lot of uncharacterized things, that’s where machine learning can be really useful, because we’re just looking for patterns — but that’s not the end goal. What we hope to do is to map these patterns to evolutionary relationships between each genome, each microbe, and each instance of life. 

Previously, we’ve been thinking about proteins as a standalone entity — that gets us to a decent degree of information because proteins are related by homology, and therefore things that are evolutionarily related might have a similar function. 

What is known about microbiology is that proteins are encoded into genomes, and the context in which that protein is bounded — what regions come before and after — is evolutionarily conserved, especially if there is a functional coupling. This makes total sense because when you have three proteins that need to be expressed together because they form a unit, then you might want them located right next to each other. 

What I want to do is incorporate more of that genomic context in the way that we search for and annotate proteins and understand protein function, so that we can go beyond sequence or structural similarity to add contextual information to how we understand proteins and hypothesize about their functions. 

Q: How can your research be applied to harnessing the functional potential of microbes? 

A: Microbes are possibly the world’s best chemists. Leveraging microbial metabolism and biochemistry will lead to more sustainable and more efficient methods for producing new materials, new therapeutics, and new types of polymers. 

But it’s not just about efficiency — microbes are doing chemistry we don’t even know how to think about. Understanding how microbes work, and being able to understand their genomic makeup and their functional capacity, will also be really important as we think about how our world and climate are changing. A majority of carbon sequestration and nutrient cycling is undertaken by microbes; if we don’t understand how a given microbe is able to fix nitrogen or carbon, then we will face difficulties in modeling the nutrient fluxes of the Earth. 

On the more therapeutic side, infectious diseases are a real and growing threat. Understanding how microbes behave in diverse environments relative to the rest of our microbiome is really important as we think about the future and combating microbial pathogens.