MIT spinout Commonwealth Fusion Systems unveils plans for the world’s first fusion power plant

America is one step closer to tapping into a new and potentially limitless clean energy source today, with the announcement from MIT spinout Commonwealth Fusion Systems (CFS) that it plans to build the world’s first grid-scale fusion power plant in Chesterfield County, Virginia.

The announcement is the latest milestone for the company, which has made groundbreaking progress toward harnessing fusion — the reaction that powers the sun — since its founders first conceived of their approach in an MIT classroom in 2012. CFS is now commercializing a suite of advanced technologies developed in MIT research labs.

“This moment exemplifies the power of MIT’s mission, which is to create knowledge that serves the nation and the world, whether via the classroom, the lab, or out in communities,” MIT Vice President for Research Ian Waitz says. “From student coursework 12 years ago to today’s announcement of the siting in Virginia of the world’s first fusion power plant, progress has been amazingly rapid. At the same time, we owe this progress to over 65 years of sustained investment by the U.S. federal government in basic science and energy research.”

The new fusion power plant, named ARC, is expected to come online in the early 2030s and generate about 400 megawatts of clean, carbon-free electricity — enough energy to power large industrial sites or about 150,000 homes.

The plant will be built at the James River Industrial Park outside of Richmond through a nonfinancial collaboration with Dominion Energy Virginia, which will provide development and technical expertise along with leasing rights for the site. CFS will independently finance, build, own, and operate the power plant.

The plant will support Virginia’s economic and clean energy goals by generating what is expected to be billions of dollars in economic development and hundreds of jobs during its construction and long-term operation.

More broadly, ARC will position the U.S. to lead the world in harnessing a new form of safe and reliable energy that could prove critical for economic prosperity and national security, including for meeting increasing electricity demands driven by needs like artificial intelligence.

“This will be a watershed moment for fusion,” says CFS co-founder Dennis Whyte, the Hitachi America Professor of Engineering at MIT. “It sets the pace in the race toward commercial fusion power plants. The ambition is to build thousands of these power plants and to change the world.”

Fusion can generate energy from abundant fuels like hydrogen and lithium isotopes, which can be sourced from seawater, and leave behind no emissions or toxic waste. However, harnessing fusion in a way that produces more power than it takes in has proven difficult because of the high temperatures needed to create and maintain the fusion reaction. Over the course of decades, scientists and engineers have worked to make the dream of fusion power plants a reality.

In 2012, teaching the MIT class 22.63 (Principles of Fusion Engineering), Whyte challenged a group of graduate students to design a fusion device that would use a new kind of superconducting magnet to confine the plasma used in the reaction. It turned out the magnets enabled a more compact and economic reactor design. When Whyte reviewed his students’ work, he realized that could mean a new development path for fusion.

Since then, a huge amount of capital and expertise has rushed into the once fledgling fusion industry. Today there are dozens of private fusion companies around the world racing to develop the first net-energy fusion power plants, many utilizing the new superconducting magnets. CFS, which Whyte founded with several students from his class, has attracted more than $2 billion in funding.

“It all started with that class, where our ideas kept evolving as we challenged the standard assumptions that came with fusion,” Whyte says. “We had this new superconducting technology, so much of the common wisdom was no longer valid. It was a perfect forum for students, who can challenge the status quo.”

Since the company’s founding in 2017, it has collaborated with researchers in MIT’s Plasma Science and Fusion Center (PFSC) on a range of initiatives, from validating the underlying plasma physics for the first demonstration machine to breaking records with a new kind of magnet to be used in commercial fusion power plants. Each piece of progress moves the U.S. closer to harnessing a revolutionary new energy source.

CFS is currently completing development of its fusion demonstration machine, SPARC, at its headquarters in Devens, Massachusetts. SPARC is expected to produce its first plasma in 2026 and net fusion energy shortly after, demonstrating for the first time a commercially relevant design that will produce more power than it consumes. SPARC will pave the way for ARC, which is expected to deliver power to the grid in the early 2030s.

“There’s more challenging engineering and science to be done in this field, and we’re very enthusiastic about the progress that CFS and the researchers on our campus are making on those problems,” Waitz says. “We’re in a ‘hockey stick’ moment in fusion energy, where things are moving incredibly quickly now. On the other hand, we can’t forget about the much longer part of that hockey stick, the sustained support for very complex, fundamental research that underlies great innovations. If we’re going to continue to lead the world in these cutting-edge technologies, continued investment in those areas will be crucial.”

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MIT researchers introduce Boltz-1, a fully open-source model for predicting biomolecular structures

MIT scientists have released a powerful, open-source AI model, called Boltz-1, that could significantly accelerate biomedical research and drug development.

Developed by a team of researchers in the MIT Jameel Clinic for Machine Learning in Health, Boltz-1 is the first fully open-source model that achieves state-of-the-art performance at the level of AlphaFold3, the model from Google DeepMind that predicts the 3D structures of proteins and other biological molecules.

MIT graduate students Jeremy Wohlwend and Gabriele Corso were the lead developers of Boltz-1, along with MIT Jameel Clinic Research Affiliate Saro Passaro and MIT professors of electrical engineering and computer science Regina Barzilay and Tommi Jaakkola. Wohlwend and Corso presented the model at a Dec. 5 event at MIT’s Stata Center, where they said their ultimate goal is to foster global collaboration, accelerate discoveries, and provide a robust platform for advancing biomolecular modeling.

“We hope for this to be a starting point for the community,” Corso said. “There is a reason we call it Boltz-1 and not Boltz. This is not the end of the line. We want as much contribution from the community as we can get.”

Proteins play an essential role in nearly all biological processes. A protein’s shape is closely connected with its function, so understanding a protein’s structure is critical for designing new drugs or engineering new proteins with specific functionalities. But because of the extremely complex process by which a protein’s long chain of amino acids is folded into a 3D structure, accurately predicting that structure has been a major challenge for decades.

DeepMind’s AlphaFold2, which earned Demis Hassabis and John Jumper the 2024 Nobel Prize in Chemistry, uses machine learning to rapidly predict 3D protein structures that are so accurate they are indistinguishable from those experimentally derived by scientists. This open-source model has been used by academic and commercial research teams around the world, spurring many advancements in drug development.

AlphaFold3 improves upon its predecessors by incorporating a generative AI model, known as a diffusion model, which can better handle the amount of uncertainty involved in predicting extremely complex protein structures. Unlike AlphaFold2, however, AlphaFold3 is not fully open source, nor is it available for commercial use, which prompted criticism from the scientific community and kicked off a global race to build a commercially available version of the model.

For their work on Boltz-1, the MIT researchers followed the same initial approach as AlphaFold3, but after studying the underlying diffusion model, they explored potential improvements. They incorporated those that boosted the model’s accuracy the most, such as new algorithms that improve prediction efficiency.

Along with the model itself, they open-sourced their entire pipeline for training and fine-tuning so other scientists can build upon Boltz-1.

“I am immensely proud of Jeremy, Gabriele, Saro, and the rest of the Jameel Clinic team for making this release happen. This project took many days and nights of work, with unwavering determination to get to this point. There are many exciting ideas for further improvements and we look forward to sharing them in the coming months,” Barzilay says.

It took the MIT team four months of work, and many experiments, to develop Boltz-1. One of their biggest challenges was overcoming the ambiguity and heterogeneity contained in the Protein Data Bank, a collection of all biomolecular structures that thousands of biologists have solved in the past 70 years.

“I had a lot of long nights wrestling with these data. A lot of it is pure domain knowledge that one just has to acquire. There are no shortcuts,” Wohlwend says.

In the end, their experiments show that Boltz-1 attains the same level of accuracy as AlphaFold3 on a diverse set of complex biomolecular structure predictions.

“What Jeremy, Gabriele, and Saro have accomplished is nothing short of remarkable. Their hard work and persistence on this project has made biomolecular structure prediction more accessible to the broader community and will revolutionize advancements in molecular sciences,” says Jaakkola.

The researchers plan to continue improving the performance of Boltz-1 and reduce the amount of time it takes to make predictions. They also invite researchers to try Boltz-1 on their GitHub repository and connect with fellow users of Boltz-1 on their Slack channel.

“We think there is still many, many years of work to improve these models. We are very eager to collaborate with others and see what the community does with this tool,” Wohlwend adds.

Mathai Mammen, CEO and president of Parabilis Medicines, calls Boltz-1 a “breakthrough” model. “By open sourcing this advance, the MIT Jameel Clinic and collaborators are democratizing access to cutting-edge structural biology tools,” he says. “This landmark effort will accelerate the creation of life-changing medicines. Thank you to the Boltz-1 team for driving this profound leap forward!”

“Boltz-1 will be enormously enabling, for my lab and the whole community,” adds Jonathan Weissman, an MIT professor of biology and member of the Whitehead Institute for Biomedical Engineering who was not involved in the study. “We will see a whole wave of discoveries made possible by democratizing this powerful tool.” Weissman adds that he anticipates that the open-source nature of Boltz-1 will lead to a vast array of creative new applications.

This work was also supported by a U.S. National Science Foundation Expeditions grant; the Jameel Clinic; the U.S. Defense Threat Reduction Agency Discovery of Medical Countermeasures Against New and Emerging (DOMANE) Threats program; and the MATCHMAKERS project supported by the Cancer Grand Challenges partnership financed by Cancer Research UK and the U.S. National Cancer Institute.

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A new method to detect dehydration in plants

Have you ever wondered if your plants were dry and dehydrated, or if you’re not watering them enough? Farmers and green-fingered enthusiasts alike may soon have a way to find this out in real-time. 

Over the past decade, researchers have been working on sensors to detect a wide range of chemical compounds, and a critical bottleneck has been developing sensors that can be used within living biological systems. This is all set to change with new sensors by the Singapore-MIT Alliance for Research and Technology (SMART) that can detect pH changes in living plants — an indicator of drought stress in plants — and enable the timely detection and management of drought stress before it leads to irreversible yield loss.

Researchers from the Disruptive and Sustainable Technologies for Agricultural Precision (DiSTAP) interdisciplinary research group of SMART, MIT’s research enterprise in Singapore, in collaboration with Temasek Life Sciences Laboratory and MIT, have pioneered the world’s first covalent organic framework (COF) sensors integrated within silk fibroin (SF) microneedles for in-planta detection of physiological pH changes. This advanced technology can detect a reduction in acidity in plant xylem tissues, providing early warning of drought stress in plants up to 48 hours before traditional methods.

Drought — or a lack of water — is a significant stressor that leads to lower yield by affecting key plant metabolic pathways, reducing leaf size, stem extension, and root proliferation. If prolonged, it can eventually cause plants to become discolored, wilt, and die. As agricultural challenges — including those posed by climate change, rising costs, and lack of land space — continue to escalate and adversely affect crop production and yield, farmers are often unable to implement proactive measures or pre-symptomatic diagnosis for early and timely intervention. This underscores the need for improved sensor integration that can facilitate in-vivo assessments and timely interventions in agricultural practices.

“This type of sensor can be easily attached to the plant and queried with simple instrumentation. It can therefore bring powerful analyses, like the tools we are developing within DISTAP, into the hands of farmers and researchers alike,” says Professor Michael Strano, co-corresponding author, DiSTAP co-lead principal investigator, and the Carbon P. Dubbs Professor of Chemical Engineering at MIT.

SMART’s breakthrough addresses a long-standing challenge for COF-based sensors, which were — until now — unable to interact with biological tissues. COFs are networks of organic molecules or polymers — which contain carbon atoms bonded to elements like hydrogen, oxygen, or nitrogen — arranged into consistent, crystal-like structures, which change color according to different pH levels. As drought stress can be detected through pH level changes in plant tissues, this novel COF-based sensor allows early detection of drought stress in plants through real-time measuring of pH levels in plant xylem tissues. This method could help farmers optimize crop production and yield amid evolving climate patterns and environmental conditions.

“The COF-silk sensors provide an example of new tools that are required to make agriculture more precise in a world that strives to increase global food security under the challenges imposed by climate change, limited resources, and the need to reduce the carbon footprint. The seamless integration between nanosensors and biomaterials enables the effortless measurement of plant fluids’ key parameters, such as pH, that in turn allows us to monitor plant health,” says Professor Benedetto Marelli, co-corresponding author, principal investigator at DiSTAP, and associate professor of civil and environmental engineering at MIT.

In an open-access paper titled, “Chromatic Covalent Organic Frameworks Enabling In-Vivo Chemical Tomography” recently published in Nature Communications, DiSTAP researchers documented their groundbreaking work, which demonstrated the real-time detection of pH changes in plant tissues. Significantly, this method allows in-vivo 3D mapping of pH levels in plant tissues using only a smartphone camera, offering a minimally invasive approach to exploring previously inaccessible environments compared to slower and more destructive traditional optical methods.

DiSTAP researchers designed and synthesized four COF compounds that showcase tunable acid chromism — color changes associated with changing pH levels — with SF microneedles coated with a layer of COF film made of these compounds. In turn, the transparency of SF microneedles and COF film allows in-vivo observation and visualization of pH spatial distributions through changes in the pH-sensitive colors.

“Building on our previous work with biodegradable COF-SF films capable of sensing food spoilage, we’ve developed a method to detect pH changes in plant tissues. When used in plants, the COF compounds will transition from dark red to red as the pH increases in the xylem tissues, indicating that the plants are experiencing drought stress and require early intervention to prevent yield loss,” says Song Wang, research scientist at SMART DiSTAP and co-first author.

“SF microneedles are robust and can be designed to remain stable even when interfacing with biological tissues. They are also transparent, which allows multidimensional mapping in a minimally invasive manner. Paired with the COF films, farmers now have a precision tool to monitor plant health in real time and better address challenges like drought and improve crop resilience,” says Yangyang Han, senior postdoc at SMART DiSTAP and co-first author.

This study sets the foundation for future design and development for COF-SF microneedle-based tomographic chemical imaging of plants with COF-based sensors. Building on this research, DiSTAP researchers will work to advance this innovative technology beyond pH detection, with a focus on sensing a broad spectrum of biologically relevant analytes such as plant hormones and metabolites.

The research is conducted by SMART and supported by the National Research Foundation of Singapore under its Campus for Research Excellence And Technological Enterprise program.

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Study reveals AI chatbots can detect race, but racial bias reduces response empathy

With the cover of anonymity and the company of strangers, the appeal of the digital world is growing as a place to seek out mental health support. This phenomenon is buoyed by the fact that over 150 million people in the United States live in federally designated mental health professional shortage areas.

“I really need your help, as I am too scared to talk to a therapist and I can’t reach one anyways.”

“Am I overreacting, getting hurt about husband making fun of me to his friends?”

“Could some strangers please weigh in on my life and decide my future for me?”

The above quotes are real posts taken from users on Reddit, a social media news website and forum where users can share content or ask for advice in smaller, interest-based forums known as “subreddits.” 

Using a dataset of 12,513 posts with 70,429 responses from 26 mental health-related subreddits, researchers from MIT, New York University (NYU), and University of California Los Angeles (UCLA) devised a framework to help evaluate the equity and overall quality of mental health support chatbots based on large language models (LLMs) like GPT-4. Their work was recently published at the 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP).

To accomplish this, researchers asked two licensed clinical psychologists to evaluate 50 randomly sampled Reddit posts seeking mental health support, pairing each post with either a Redditor’s real response or a GPT-4 generated response. Without knowing which responses were real or which were AI-generated, the psychologists were asked to assess the level of empathy in each response.

Mental health support chatbots have long been explored as a way of improving access to mental health support, but powerful LLMs like OpenAI’s ChatGPT are transforming human-AI interaction, with AI-generated responses becoming harder to distinguish from the responses of real humans.

Despite this remarkable progress, the unintended consequences of AI-provided mental health support have drawn attention to its potentially deadly risks; in March of last year, a Belgian man died by suicide as a result of an exchange with ELIZA, a chatbot developed to emulate a psychotherapist powered with an LLM called GPT-J. One month later, the National Eating Disorders Association would suspend their chatbot Tessa, after the chatbot began dispensing dieting tips to patients with eating disorders.

Saadia Gabriel, a recent MIT postdoc who is now a UCLA assistant professor and first author of the paper, admitted that she was initially very skeptical of how effective mental health support chatbots could actually be. Gabriel conducted this research during her time as a postdoc at MIT in the Healthy Machine Learning Group, led Marzyeh Ghassemi, an MIT associate professor in the Department of Electrical Engineering and Computer Science and MIT Institute for Medical Engineering and Science who is affiliated with the MIT Abdul Latif Jameel Clinic for Machine Learning in Health and the Computer Science and Artificial Intelligence Laboratory.

What Gabriel and the team of researchers found was that GPT-4 responses were not only more empathetic overall, but they were 48 percent better at encouraging positive behavioral changes than human responses.

However, in a bias evaluation, the researchers found that GPT-4’s response empathy levels were reduced for Black (2 to 15 percent lower) and Asian posters (5 to 17 percent lower) compared to white posters or posters whose race was unknown. 

To evaluate bias in GPT-4 responses and human responses, researchers included different kinds of posts with explicit demographic (e.g., gender, race) leaks and implicit demographic leaks. 

An explicit demographic leak would look like: “I am a 32yo Black woman.”

Whereas an implicit demographic leak would look like: “Being a 32yo girl wearing my natural hair,” in which keywords are used to indicate certain demographics to GPT-4.

With the exception of Black female posters, GPT-4’s responses were found to be less affected by explicit and implicit demographic leaking compared to human responders, who tended to be more empathetic when responding to posts with implicit demographic suggestions.

“The structure of the input you give [the LLM] and some information about the context, like whether you want [the LLM] to act in the style of a clinician, the style of a social media post, or whether you want it to use demographic attributes of the patient, has a major impact on the response you get back,” Gabriel says.

The paper suggests that explicitly providing instruction for LLMs to use demographic attributes can effectively alleviate bias, as this was the only method where researchers did not observe a significant difference in empathy across the different demographic groups.

Gabriel hopes this work can help ensure more comprehensive and thoughtful evaluation of LLMs being deployed in clinical settings across demographic subgroups.

“LLMs are already being used to provide patient-facing support and have been deployed in medical settings, in many cases to automate inefficient human systems,” Ghassemi says. “Here, we demonstrated that while state-of-the-art LLMs are generally less affected by demographic leaking than humans in peer-to-peer mental health support, they do not provide equitable mental health responses across inferred patient subgroups ... we have a lot of opportunity to improve models so they provide improved support when used.”

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New climate chemistry model finds “non-negligible” impacts of potential hydrogen fuel leakage

As the world looks for ways to stop climate change, much discussion focuses on using hydrogen instead of fossil fuels, which emit climate-warming greenhouse gases (GHGs) when they’re burned. The idea is appealing. Burning hydrogen doesn’t emit GHGs to the atmosphere, and hydrogen is well-suited for a variety of uses, notably as a replacement for natural gas in industrial processes, power generation, and home heating.

But while burning hydrogen won’t emit GHGs, any hydrogen that’s leaked from pipelines or storage or fueling facilities can indirectly cause climate change by affecting other compounds that are GHGs, including tropospheric ozone and methane, with methane impacts being the dominant effect. A much-cited 2022 modeling study analyzing hydrogen’s effects on chemical compounds in the atmosphere concluded that these climate impacts could be considerable. With funding from the MIT Energy Initiative’s Future Energy Systems Center, a team of MIT researchers took a more detailed look at the specific chemistry that poses the risks of using hydrogen as a fuel if it leaks.

The researchers developed a model that tracks many more chemical reactions that may be affected by hydrogen and includes interactions among chemicals. Their open-access results, published Oct. 28 in Frontiers in Energy Research, showed that while the impact of leaked hydrogen on the climate wouldn’t be as large as the 2022 study predicted — and that it would be about a third of the impact of any natural gas that escapes today — leaked hydrogen will impact the climate. Leak prevention should therefore be a top priority as the hydrogen infrastructure is built, state the researchers.

Hydrogen’s impact on the “detergent” that cleans our atmosphere

Global three-dimensional climate-chemistry models using a large number of chemical reactions have also been used to evaluate hydrogen’s potential climate impacts, but results vary from one model to another, motivating the MIT study to analyze the chemistry. Most studies of the climate effects of using hydrogen consider only the GHGs that are emitted during the production of the hydrogen fuel. Different approaches may make “blue hydrogen” or “green hydrogen,” a label that relates to the GHGs emitted. Regardless of the process used to make the hydrogen, the fuel itself can threaten the climate. For widespread use, hydrogen will need to be transported, distributed, and stored — in short, there will be many opportunities for leakage. 

The question is, What happens to that leaked hydrogen when it reaches the atmosphere? The 2022 study predicting large climate impacts from leaked hydrogen was based on reactions between pairs of just four chemical compounds in the atmosphere. The results showed that the hydrogen would deplete a chemical species that atmospheric chemists call the “detergent of the atmosphere,” explains Candice Chen, a PhD candidate in MIT’s Department of Earth, Atmospheric and Planetary Sciences (EAPS). “It goes around zapping greenhouse gases, pollutants, all sorts of bad things in the atmosphere. So it’s cleaning our air.” Best of all, that detergent — the hydroxyl radical, abbreviated as OH — removes methane, which is an extremely potent GHG in the atmosphere. OH thus plays an important role in slowing the rate at which global temperatures rise. But any hydrogen leaked to the atmosphere would reduce the amount of OH available to clean up methane, so the concentration of methane would increase.

However, chemical reactions among compounds in the atmosphere are notoriously complicated. While the 2022 study used a “four-equation model,” Chen and her colleagues — Susan Solomon, the Lee and Geraldine Martin Professor of Environmental Studies and Chemistry; and Kane Stone, a research scientist in EAPS — developed a model that includes 66 chemical reactions. Analyses using their 66-equation model showed that the four-equation system didn’t capture a critical feedback involving OH — a feedback that acts to protect the methane-removal process.

Here’s how that feedback works: As the hydrogen decreases the concentration of OH, the cleanup of methane slows down, so the methane concentration increases. However, that methane undergoes chemical reactions that can produce new OH radicals. “So the methane that’s being produced can make more of the OH detergent,” says Chen. “There’s a small countering effect. Indirectly, the methane helps produce the thing that’s getting rid of it.” And, says Chen, that’s a key difference between their 66-equation model and the four-equation one. “The simple model uses a constant value for the production of OH, so it misses that key OH-production feedback,” she says.

To explore the importance of including that feedback effect, the MIT researchers performed the following analysis: They assumed that a single pulse of hydrogen was injected into the atmosphere and predicted the change in methane concentration over the next 100 years, first using four-equation model and then using the 66-equation model. With the four-equation system, the additional methane concentration peaked at nearly 2 parts per billion (ppb); with the 66-equation system, it peaked at just over 1 ppb.

Because the four-equation analysis assumes only that the injected hydrogen destroys the OH, the methane concentration increases unchecked for the first 10 years or so. In contrast, the 66-equation analysis goes one step further: the methane concentration does increase, but as the system re-equilibrates, more OH forms and removes methane. By not accounting for that feedback, the four-equation analysis overestimates the peak increase in methane due to the hydrogen pulse by about 85 percent. Spread over time, the simple model doubles the amount of methane that forms in response to the hydrogen pulse.

Chen cautions that the point of their work is not to present their result as “a solid estimate” of the impact of hydrogen. Their analysis is based on a simple “box” model that represents global average conditions and assumes that all the chemical species present are well mixed. Thus, the species can vary over time — that is, they can be formed and destroyed — but any species that are present are always perfectly mixed. As a result, a box model does not account for the impact of, say, wind on the distribution of species. “The point we're trying to make is that you can go too simple,” says Chen. “If you’re going simpler than what we're representing, you will get further from the right answer.” She goes on to note, “The utility of a relatively simple model like ours is that all of the knobs and levers are very clear. That means you can explore the system and see what affects a value of interest.”

Leaked hydrogen versus leaked natural gas: A climate comparison

Burning natural gas produces fewer GHG emissions than does burning coal or oil; but as with hydrogen, any natural gas that’s leaked from wells, pipelines, and processing facilities can have climate impacts, negating some of the perceived benefits of using natural gas in place of other fossil fuels. After all, natural gas consists largely of methane, the highly potent GHG in the atmosphere that’s cleaned up by the OH detergent. Given its potency, even small leaks of methane can have a large climate impact.

So when thinking about replacing natural gas fuel — essentially methane — with hydrogen fuel, it’s important to consider how the climate impacts of the two fuels compare if and when they’re leaked. The usual way to compare the climate impacts of two chemicals is using a measure called the global warming potential, or GWP. The GWP combines two measures: the radiative forcing of a gas — that is, its heat-trapping ability — with its lifetime in the atmosphere. Since the lifetimes of gases differ widely, to compare the climate impacts of two gases, the convention is to relate the GWP of each one to the GWP of carbon dioxide. 

But hydrogen and methane leakage cause increases in methane, and that methane decays according to its lifetime. Chen and her colleagues therefore realized that an unconventional procedure would work: they could compare the impacts of the two leaked gases directly. What they found was that the climate impact of hydrogen is about three times less than that of methane (on a per mass basis). So switching from natural gas to hydrogen would not only eliminate combustion emissions, but also potentially reduce the climate effects, depending on how much leaks.

Key takeaways

In summary, Chen highlights some of what she views as the key findings of the study. First on her list is the following: “We show that a really simple four-equation system is not what should be used to project out the atmospheric response to more hydrogen leakages in the future.” The researchers believe that their 66-equation model is a good compromise for the number of chemical reactions to include. It generates estimates for the GWP of methane “pretty much in line with the lower end of the numbers that most other groups are getting using much more sophisticated climate chemistry models,” says Chen. And it’s sufficiently transparent to use in exploring various options for protecting the climate. Indeed, the MIT researchers plan to use their model to examine scenarios that involve replacing other fossil fuels with hydrogen to estimate the climate benefits of making the switch in coming decades.

The study also demonstrates a valuable new way to compare the greenhouse effects of two gases. As long as their effects exist on similar time scales, a direct comparison is possible — and preferable to comparing each with carbon dioxide, which is extremely long-lived in the atmosphere. In this work, the direct comparison generates a simple look at the relative climate impacts of leaked hydrogen and leaked methane — valuable information to take into account when considering switching from natural gas to hydrogen.

Finally, the researchers offer practical guidance for infrastructure development and use for both hydrogen and natural gas. Their analyses determine that hydrogen fuel itself has a “non-negligible” GWP, as does natural gas, which is mostly methane. Therefore, minimizing leakage of both fuels will be necessary to achieve net-zero carbon emissions by 2050, the goal set by both the European Commission and the U.S. Department of State. Their paper concludes, “If used nearly leak-free, hydrogen is an excellent option. Otherwise, hydrogen should only be a temporary step in the energy transition, or it must be used in tandem with carbon-removal steps [elsewhere] to counter its warming effects.”

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Lara Ozkan named 2025 Marshall Scholar

Lara Ozkan, an MIT senior from Oradell, New Jersey, has been selected as a 2025 Marshall Scholar and will begin graduate studies in the United Kingdom next fall. Funded by the British government, the Marshall Scholarship awards American students of high academic achievement with the opportunity to pursue graduate studies in any field at any university in the U.K. Up to 50 scholarships are granted each year.

“We are so proud that Lara will be representing MIT in the U.K.,” says Kim Benard, associate dean of distinguished fellowships. “Her accomplishments to date have been extraordinary and we are excited to see where her future work goes.” Ozkan, along with MIT’s other endorsed Marshall candidates, was mentored by the distinguished fellowships team in Career Advising and Professional Development, and the Presidential Committee on Distinguished Fellowships, co-chaired by professors Nancy Kanwisher and Tom Levenson. 

Ozkan, a senior majoring in computer science and molecular biology, plans to pursue through her Marshall Scholarship an MPhil in biological science at Cambridge University’s Sanger Institute, followed by a master’s by research degree in artificial intelligence and machine learning at Imperial College London. She is committed to a career advancing women’s health through innovation in technology and the application of computational tools to research.

Prior to beginning her studies at MIT, Ozkan conducted computational biology research at Cold Spring Harbor Laboratory. At MIT, she has been an undergraduate researcher with the MIT Media Lab’s Conformable Decoders group, where she has worked on breast cancer wearable ultrasound technologies. She also contributes to Professor Manolis Kellis’ computational biology research group in the MIT Computer Science and Artificial Intelligence Laboratory. Ozkan’s achievements in computational biology research earned her the MIT Susan Hockfield Prize in Life Sciences.

At the MIT Schwarzman College of Computing, Ozkan has examined the ethical implications of genomics projects and developed AI ethics curricula for MIT computer science courses. Through internships with Accenture Gen AI Risk and pharmaceutical firms, she gained practical insights into responsible AI use in health care.

Ozkan is president and executive director of MIT Capital Partners, an organization that connects the entrepreneurship community with venture capital firms, and she is president of the MIT Sloan Business Club. Additionally, she serves as an undergraduate research peer ambassador and is a member of the MIT EECS Committee on Diversity, Equity, and Inclusion. As part of the MIT Schwarzman College of Computing Undergraduate Advisory Group, she advises on policies and programming to improve the student experience in interdisciplinary computing.

Beyond Ozkan’s research roles, she volunteers with MIT CodeIt, teaching middle-school girls computer science. As a counselor with Camp Kesem, she mentors children whose parents are impacted by cancer.

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Street smarts

Dozens of major research labs dot the streets of Kendall Square, a Cambridge, Massachusetts, neighborhood in which MIT partially sits. But for Andres Sevtsuk’s City Form Lab, the streets of Kendall Square themselves, and those in other cities, are subjects for research.

Sevtsuk is an associate professor of urban science and planning at MIT and a leading expert in urban form and spatial analysis. His work examines how the design of built environments affects social life within them. The way cities are structured influences whether street-level retail commerce can thrive, whether and how much people walk, and how much they encounter each other face to face.

“City environments that allow us to get more things done on foot tend to not only make people healthier, but they are more sustainable in terms of emissions and energy use, and they provide more social encounters between different members of society, which is fundamental to democracy,” Sevtsuk says.

However, many things Sevtsuk studies do not come with much pre-existing data. While some aspects of cities are studied extensively — vehicle traffic, for instance — fewer people have studied how urban planning affects walking and cycling, which most city governments seek to increase. 

To counter this trend, several years ago Sevtsuk and some research assistants began studying foot traffic in several cities, as well as Kendall Square — how much people walk, where they go, and why. Most urban walking trips are destination-driven: People go to offices, eateries, and transit stops. But a lot of pedestrian activity is also recreational and social, such as sitting in a square, people-watching, and window-shopping. Eventually Sevtsuk emerged with an innovative model of pedestrian activity, which is based around these spatial networks of interaction and calibrated to observed people counts.

He and his colleagues then scaled up their model and took it to major cities around the world, starting with the whole downtown of Melbourne, Australia. The model now includes detailed street characteristics — sidewalk dimensions, the presence of ground floor businesses, landscaping, and more — and Sevtsuk has also helped apply it to Beirut and, most recently, New York City.

The project is typical of Sevtsuk’s research, which creates new ways to bring data to urban design. In 2023, Sevtsuk and his colleagues also released a novel open-source tool, called TILE2NET, to automatically map city sidewalks from aerial imagery. He has even studied interactions on the MIT campus, in a 2022 paper quantifying how spatial relatedness between departments and centers affects communications among them.

“Applying spatial analytics to city design is timely today because when it comes to cutting carbon emissions and energy consumption, or improving public health, or supporting local business on city streets, they relate to how cities are configured,” Sevtsuk says. “Urban designers have historically not been very focused on quantifying those effects. But studying these dynamics can help us understand how social interactions in cities work and how proposed interventions may impact a community.”

For his research and teaching, Sevtsuk received tenure at MIT earlier this year.

Growing and living in cities

Sevtsuk is originally from Tartu, Estonia, where his experiences helped attune him to the street life of cities.

“I do think where I come from enhanced my interest in urban design,” Sevtsuk says. “I grew up in public housing. That very much framed my appreciation for public amenities. Your home was where you slept, but everything else, where you played as a child or found cultural entertainment as a teenager, was in the public sphere of the city.”

Initially interested in studying architecture, Sevtsuk received a BArch degree from the Estonian Academy of Arts, then a BArch from the Ecole d’Architecture de la Ville et des Territoires, in Paris. Over time, he became increasingly interested in city design and planning, and enrolled as a master’s student at MIT, earning his SMArchS degree in 2006 while studying how technology could help us better understand urban social processes.

“MIT had a very strong research orientation for even masters-level students,” Sevtsuk says. “It is famous for that. I came because I was drawn to the opportunity to get hands-on into research around city design.”

Sevtsuk stayed at MIT for his doctoral studies, earning his PhD in 2010, with the late William Mitchell as his principal advisor. “Bill was interested in the influence of technology on cities,” says Sevtsuk, who appreciated the wide-ranging intellectual milieu that sprang up around Mitchell. “A lot of fascinating and intellectually experimental people gravitated around Bill.”

With his PhD in hand, Sevtstuk then joined an MIT collaboration at the new Singapore University of Technology and Design, a couple of years after it first opened.

“That was a lot of fun, building a new university, and we were teaching the first cohort and first courses,” Sevtsuk says. “It was an exciting project.”

Living in Asia also helped open doors for some hands-on research in Singapore and Indonesia, where Sevtsuk worked with city governments and the World Bank on urban planning and design projects in several cities.

“There was not a lot of data, and yet we had to think about how spatial analyses could be deployed to support planning decisions,” Sevtsuk says. “It forced you to think how to apply methods without abundant data in the traditional sense. In retrospect some of the software around pedestrian modeling we developed was influenced by these constraints, from understanding the minimum data inputs needed to capture people’s mobility dynamics in a neighborhood.”

From Melbourne to the Infinite Corridor

Returning to the U.S., Sevtsuk took a faculty position at Harvard University’s Graduate School of Design in 2015. He then joined the MIT faculty in 2019.

Throughout his career, Sevtsuk’s projects have consistently added insight to existing data or created all-new repositories of data for wider use. His team’s work in Melbourne leveraged a rare case of a city with copious pedestrian data of its own. There, Sevtsuk found the model not only explained foot traffic patterns but could also be used to forecast how changes in the built environment, such as new development projects, could affect foot traffic in different parts of the city.

In Beirut, the modeling work on improving community streets is part of post-disaster recovery after the Beirut port explosion of 2020. In New York, Sevtsuk and his colleagues are studying the largest pedestrian network in the U.S., covering all five boroughs of the city. The TILE2NET project, meanwhile, provides information for planners and experts in an area — sidewalk mapping — which most places do not have data on either.

When it came to studying the MIT campus, Sevtsuk brought new a new approach to a subject with an Institute legacy: An earlier campus professor, Thomas Allen of the MIT Sloan School of Management, did pioneering research about workspace design and collaboration. Sevtsuk and his team, however, looked at the larger campus as a network.

Linking spatial relations and email communication, they found that not only does the level of interaction between MIT departments and labs increase when those units are spatially closer to each other, but it also increases when their members are more likely to walk past each other’s offices on their daily routes to work or when they patronize the same eateries on campus.

Urban design for the people

Sevtsuk thinks about his own work as being not just data-driven but part of a larger refashioning of the field of urban design. In American cities, urban design may still be associated with the large-scale redevelopment of neighborhoods that took place in the first few postwar decades: massive freeways tearing through cities and dislocating older business districts, and large housing and office projects undertaken in the name of modernization and tax revenue increases but not in the interests of existing residents and workers. Many of these projects were disastrous for urban communities.

By the 1960s and 1970s, urban planning programs around the country attempted to quell the inadequacy of large-scale urban design and instead focused on the social and economic needs of communities first. The role of urban design was somewhat sidelined in this transition. But instead of giving up on urban design as a tool for community improvement, Sevtsuk thinks that planning and urban design research can help uncover the important ways in which design can support communities in their daily lives as much as community development initiatives and policies can.

“There was a turn in the field of planning away from urban design as a central area of focus, toward more sociologically grounded community-driven approaches,” Sevtsuk says. “And for good reasons. But during these decades, some of the most anti-urban, car-oriented, and resource-intensive built environments in the U.S. were created, which we now need to deal with.”

He adds: “In my work I try to quantify effects of urban design on people, from mobility outcomes, to generating social encounters, to supporting small local businesses on city streets. In my research group we try to connect urban design back to the qualities that people and communities care about. Faced with the profound climate challenges today, we must better understand the influence of urban design on society — on carbon emissions, on health, on social exchange, and even on democracy, because it’s such a critical dimension.”

A dedicated teacher, Sevtsuk works with students with broad backgrounds and interests from across the Institute. One of his main classes, 11.001 (Introduction to Urban Design and Development), draws students from many departments — including computer science, civil engineering, and management — who want to contribute to sustainable and equitable cities. He also teaches an applied class on modeling pedestrian activity, and his research group draws students and researchers from many countries.

“What resonates with students is that when we look closely at the complex organized systems of cities, we can make sense of how they work,” Sevtsuk says. “But we can also figure out how to change them, how to nudge them toward collective improvement. And many MIT students are eager to mobilize their amazing technical skills towards that quest.”

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