In biology, defects are generally bad. But in materials science, defects can be intentionally tuned to give materials useful new properties. Today, atomic-scale defects are carefully introduced during the manufacturing process of products like steel, semiconductors, and solar cells to help improve strength, control electrical conductivity, optimize performance, and more.

But even as defects have become a powerful tool, accurately measuring different types of defects and their concentrations in finished products has been challenging, especially without cutting open or damaging the final material. Without knowing what defects are in their materials, engineers risk making products that perform poorly or have unintended properties.

Now, MIT researchers have built an AI model capable of classifying and quantifying certain defects using data from a noninvasive neutron-scattering technique. The model, which was trained on 2,000 different semiconductor materials, can detect up to six kinds of point defects in a material simultaneously, something that would be impossible using conventional techniques alone.

“Existing techniques can’t accurately characterize defects in a universal and quantitative way without destroying the material,” says lead author Mouyang Cheng, a PhD candidate in the Department of Materials Science and Engineering. “For conventional techniques without machine learning, detecting six different defects is unthinkable. It’s something you can’t do any other way.”

The researchers say the model is a step toward harnessing defects more precisely in products like semiconductors, microelectronics, solar cells, and battery materials.

“Right now, detecting defects is like the saying about seeing an elephant: Each technique can only see part of it,” says senior author and associate professor of nuclear science and engineering Mingda Li. “Some see the nose, others the trunk or ears. But it is extremely hard to see the full elephant. We need better ways of getting the full picture of defects, because we have to understand them to make materials more useful.”

Joining Cheng and Li on the paper are postdoc Chu-Liang Fu, undergraduate researcher Bowen Yu, master’s student Eunbi Rha, PhD student Abhijatmedhi Chotrattanapituk ’21, and Oak Ridge National Laboratory staff members Douglas L Abernathy PhD ’93 and Yongqiang Cheng. The paper appears today in the journal Matter.

Detecting defects

Manufacturers have gotten good at tuning defects in their materials, but measuring precise quantities of defects in finished products is still largely a guessing game.

“Engineers have many ways to introduce defects, like through doping, but they still struggle with basic questions like what kind of defect they’ve created and in what concentration,” Fu says. “Sometimes they also have unwanted defects, like oxidation. They don’t always know if they introduced some unwanted defects or impurity during synthesis. It’s a longstanding challenge.”

The result is that there are often multiple defects in each material. Unfortunately, each method for understanding defects has its limits. Techniques like X-ray diffraction and positron annihilation characterize only some types of defects. Raman spectroscopy can discern the type of defect but can’t directly infer the concentration. Another technique known as transmission electron microscope requires people to cut thin slices of samples for scanning.

In a few previous papers, Li and collaborators applied machine learning to experimental spectroscopy data to characterize crystalline materials. For the new paper, they wanted to apply that technique to defects.

For their experiment, the researchers built a computational database of 2,000 semiconductor materials. They made sample pairs of each material, with one doped for defects and one left without defects, then used a neutron-scattering technique that measures the different vibrational frequencies of atoms in solid materials. They trained a machine-learning model on the results.

“That built a foundational model that covers 56 elements in the periodic table,” Cheng says. “The model leverages the multihead attention mechanism, just like what ChatGPT is using. It similarly extracts the difference in the data between materials with and without defects and outputs a prediction of what dopants were used and in what concentrations.”

The researchers fine-tuned their model, verified it on experimental data, and showed it could measure defect concentrations in an alloy commonly used in electronics and in a separate superconductor material.

The researchers also doped the materials multiple times to introduce multiple point defects and test the limits of the model, ultimately finding it can make predictions about up to six defects in materials simultaneously, with defect concentrations as low as 0.2 percent.

“We were really surprised it worked that well,” Cheng says. “It’s very challenging to decode the mixed signals from two different types of defects — let alone six.”

A model approach

Typically, manufacturers of things like semiconductors run invasive tests on a small percentage of products as they come off the manufacturing line, a slow process that limits their ability to detect every defect.

“Right now, people largely estimate the quantities of defects in their materials,” Yu says. “It is a painstaking experience to check the estimates by using each individual technique, which only offers local information in a single grain anyway. It creates misunderstandings about what defects people think they have in their material.”

The results were exciting for the researchers, but they note their technique measuring the vibrational frequencies with neutrons would be difficult for companies to quickly deploy in their own quality-control processes.

“This method is very powerful, but its availability is limited,” Rha says. “Vibrational spectra is a simple idea, but in certain setups it’s very complicated. There are some simpler experimental setups based on other approaches, like Raman spectroscopy, that could be more quickly adopted.”

Li says companies have already expressed interest in the approach and asked when it will work with Raman spectroscopy, a widely used technique that measures the scattering of light. Li says the researchers’ next step is training a similar model based on Raman spectroscopy data. They also plan to expand their approach to detect features that are larger than point defects, like grains and dislocations.

For now, though, the researchers believe their study demonstrates the inherent advantage of AI techniques for interpreting defect data.

“To the human eye, these defect signals would look essentially the same,” Li says. “But the pattern recognition of AI is good enough to discern different signals and get to the ground truth. Defects are this double-edged sword. There are many good defects, but if there are too many, performance can degrade. This opens up a new paradigm in defect science.”

The work was supported, in part, by the Department of Energy and the National Science Foundation.

A thoughtful review of Apple’s system to alert users that the camera is on. It’s really well-designed, and important in a world where malware could surreptitiously start recording.

The reason it’s tempting to think that a dedicated camera indicator light is more secure than an on-display indicator is the fact that hardware is generally more secure than software, because it’s harder to tamper with. With hardware, a dedicated hardware indicator light can be connected to the camera hardware such that if the camera is accessed, the light must turn on, with no way for software running on the device, no matter its privileges, to change that. With an indicator light that is rendered on the display, it’s not foolish to worry that malicious software, with sufficient privileges, could draw over the pixels on the display where the camera indicator is rendered, disguising that the camera is in use...

The upfront costs of transitioning to clean energy and worries about rising utility bills are forcing tough choices for Democratic leaders.

The agency is expected to soon finalize the repeal, which was proposed before the Trump administration's elimination of the 2009 endangerment finding.

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A top researcher says storms in Hawaii might have helped cause record heat in the West in an "underrecognized" phenomenon.

The Democrats say EPA Administration Lee Zeldin misled them about grant cancellations.

These companies now say they must be flexible as they rush to build sprawling data centers that can consume more power than entire cities.

The French energy giant pointed out that many scientists now say limiting global warming to 1.5 degrees Celsius above preindustrial levels is out of reach.

In climate science, tipping points are critical thresholds in the Earth’s systems which, if breached, can result in abrupt, dangerous and often irreversible weather patterns.

Carrying out such research requires specialized scuba diving skills plus the proper scientific background — qualifications only a few hundred people in the world currently have.

Nature Climate Change, Published online: 30 March 2026; doi:10.1038/s41558-026-02603-2

Retrogressive thaw slumps are a key disturbance resulting from permafrost thaw that impact both vegetation and soil carbon. This study assesses surface greenness recovery times following thaw and shows that recovery can be predicted based on annual ecosystem gross primary productivity.

Cisco Systems Case Has Major Implications for Global Human Rights

SAN FRANCISCO – U.S. technology companies should be legally accountable in U.S. courts for building tools that purposefully and actively facilitate human rights abuses by foreign governments, the Electronic Frontier Foundation argued in a brief filed Friday to the U.S. Supreme Court. 

The brief filed in the case of Cisco Systems, Inc., et al., v. Doe I, et al. urges the high court to uphold the U.S. Court of Appeals for the 9th Circuit’s 2023 ruling that U.S. corporations can be held liable under the Alien Tort Statute (ATS) – a law that lets noncitizens bring claims in U.S. federal court for international law violations – for taking actions in the U.S. that aided and abetted persecution and torture abroad. 

“This is not a case about a company that merely provided routers or other general-purpose technologies to a foreign government. It is about a company that purposefully and actively assisted in the persecution of a religious group,” the brief says. “There is a growing set of companies—including American companies—that provide surveillance technologies that are vulnerable to, and indeed are being used to, support gross human rights abuses. Because of this, the outcome of this case will have profound implications for millions of people who rely on digital technologies in their everyday lives, including to practice their religion.” 

The “Golden Shield” system that Cisco custom-built for the Chinese government was an essential component of persecution against the Falun Gong religious group—persecution that included online spying and tracking, detention, and torture. Victims reported that intercepted communications were used during torture sessions aimed at forcing them to renounce their religion. Falun Gong victims and their families sued Cisco in 2011 and a federal district judge dismissed the case in 2014. The case was delayed three times as the Supreme Court considered three prior ATS cases.   

The 9th Circuit appeals court – after proceedings including an amicus brief from EFF – reversed that lower decision, holding that U.S. corporations can be held liable under the ATS for aiding and abetting human rights abuses abroad. It also held that a company does not need to have the “purpose” to facilitate human rights abuses in order to be held liable; it only needs to have “knowledge” that its assistance helped in such abuses. It then held that the plaintiffs’ allegations showed that Cisco’s actions met both standards. The court also held that the fact that a technology has legitimate uses does not shield a company from liability for other uses that led to human rights abuses when the standards of international law are met. Taken cumulatively, Cisco’s actions in the U.S. were sufficient to allow the case to proceed, the 9th Circuit ruled.  

Cisco appealed to the Supreme Court, which granted review in January. The case, No. 24-856, is scheduled for argument on April 28. 

Cisco Systems is just one of many U.S. companies that make surveillance systems, spyware, and other products used by governments to violate people’s human rights. 

“This Court must not shut the courthouse door to victims of human rights abuses that are actively powered by American corporations,” the brief says. “In the digital age, repressive governments rarely act alone to violate human rights. They have accomplices—including technology companies that have the sophistication and technical know-how that those repressive governments lack.” 

For EFF’s amicus brief to the U.S. Supreme Court:  https://www.eff.org/document/2026-03-27-eff-amicus-brief-cisco-v-doe-scotus

For EFF’s Doe I v. Cisco case page: https://www.eff.org/cases/doe-i-v-cisco  

For the U.S. Supreme Court docket: https://www.supremecourt.gov/docket/docketfiles/html/public/24-856.html  

 

Contact:  SophiaCopeSenior Staff Attorneysophia@eff.org CindyCohnExecutive Directorcindy@eff.org

Professor Sara Prescott embodies the kind of mentorship every graduate student hopes to find: grounded in scientific rigor, guided by kindness, and defined by a deep commitment to well-being. Her approach reflects a simple but powerful belief that transformative mentorship is not only about advancing research, but about cultivating confidence, belonging, and resilience in the next generation of scholars.

A member of the 2025–27 Committed to Caring cohort, Prescott exemplifies the program’s spirit, which honors faculty who go above and beyond in nurturing both the intellectual and personal development of MIT’s graduate students.

Prescott is the Pfizer Inc. - Gerald D. Laubach Career Development Professor in the MIT departments of Biology and Brain and Cognitive Sciences, and an investigator at the Picower Institute for Learning and Memory. Her research addresses fundamental questions in body-brain communication, with a focus on lung biology, early-life adversity, women’s health, and the impacts of climate change on respiratory health.

A culture of compassion

Prescott’s mentoring philosophy begins with a focus on professional sustainability. “We cannot be effective scientists if we are unhappy or unhealthy outside of the lab,” she says. 

She pushes back against what she sees as an unhelpful narrative in academia. “There’s this idea that you must choose between a successful PhD or having a personal life. This is a false dichotomy, and a problematic attitude.” Instead, she reminds her mentees that “graduate school is a marathon, not a sprint,” encouraging them to place importance not only on their research, but also on their mental and physical well-being.

This set of values shines through within her lab climate as a whole. Students describe support for flexible scheduling and mental health leave, a willingness to reimburse meals during late-night lab sessions, and encouragement during stretches of experimental failure. In addition to these more technical supports, nominators also shared stories of Prescott engaging in the smaller details: prioritizing connection for her students, celebrating their milestones, organizing lab retreats, and fostering a culture where people feel valued beyond their productivity.

Students recognize Prescott as a safe haven within the often complex and challenging world of research. Joining Prescott’s lab was a turning point for one student who was recovering from a damaging prior mentorship experience. They arrived uncertain, struggling to trust faculty and questioning whether they belonged in science at all. Prescott met them with empathy and professionalism, offering patience and trust not just in their work, but in them as a person. They describe steady support that, over time, helped them “fall back in love with science” and envision a future they had nearly abandoned.

Prescott draws inspiration from the mentorship she received early in her career. As a trainee, she had mentors who helped her believe that she could succeed. Now in a mentoring role herself, she does her best to pass this sense of confidence on to her advisees.

She is intentional about creating space where students can grow without fear. From their very first meetings, one nominator wrote, Prescott emphasized that “graduate school is a place for learning and curiosity.” They never felt judged for not knowing something; instead, they were encouraged to ask questions, share ideas, and take intellectual risks. That environment, the student explained, allowed them to grow into their scientific identity with confidence.

Prescott reinforces this message often. Success, she tells students, grows from effort, learning, and persistence, rather than from fixed traits. When working with students, she does her best to reframe failure as part of the process, emphasizing its importance within the scientific journey. Through these avenues, she cultivates a lab culture where nominators are challenged to think boldly while feeling genuinely supported, and where her students are seen not only as researchers, but as whole people.

Advocacy beyond the bench

Prescott’s commitment to caring extends well beyond day-to-day lab work. Her nominators relate that she actively supports her students’ professional development, encouraging them to pursue writing projects, certificates, internships, leadership roles, and community engagement.

Nominators also highlight Prescott’s focus on supporting underserved communities within the field as a whole. Students highlight her involvement with Graduate Women in Biology (GwiBio), where she volunteered as a speaker for the “Glass Shards” series. Her talk “Failure as the Path to Success,” in which she candidly shared pivots and setbacks in her own career, was described as one of the organization’s most impactful sessions. 

Her dedication to inclusion is equally evident in her mentorship of scholars whose role in her lab is more temporary.  She welcomes international visiting scholars, temporary lab techs, and undergraduate interns in the MIT Summer Research Program. When one intern encountered barriers at their home institution, Prescott ensured they had a continued research home in her lab at MIT. These additional resources allowed them to complete their undergraduate thesis and graduate on time from their university.

Prescott says that she views mentorship as an evolving practice, regularly soliciting feedback from her students. Effective leadership, in her view, grows from mutual trust and open communication.

For many nominators, Prescott’s impact extends beyond their careers. “She has taught me what positive and supportive mentoring relationships look like,” one student reflected. “When I think about the type of mentor I want to be, I hope I can emulate the ways in which she supports and guides nominators to develop their scientific independence and confidence.”

In lifting up the people behind the science as thoughtfully as the science itself, Sara Prescott demonstrates that the most enduring legacy of a mentor is not only the discoveries from their lab, but the composure and courage their advisees carry forward.

During this year’s Independent Activities Period (IAP), students, researchers, and collaborators across seven time zones came together to tackle urgent technical challenges facing Ukraine as the full-scale war enters its fourth year. 

A four-week hackathon, Build for Ukraine 2.0, brought MIT students and Ukrainian collaborators into a shared innovation environment where power outages, air-raid alerts, and subzero temperatures were part of the daily reality of teamwork.

The event was co-led by the MIT-Ukraine Program, MIT Edgerton Center, and MIT Lincoln Laboratory Beaver Works, with support from Mission Innovation X, MathWorks, and MIT.nano.

Designed and taught as an IAP subject EC.S01/EC.S11 (Build for Ukraine 2026), the hackathon paired technically diverse participants with Ukrainian organizations seeking near-term solutions to problems arising directly from wartime conditions.

“It’s not every working group that has to reschedule team meetings because some members are in Ukraine and just had a blackout,” says Hosea Siu ’14, SM ’15, PhD ’18, one of the lead organizers. “This class is unusual — in the most meaningful ways.”

A collaborative class built for real-world urgency

Build for Ukraine centered on co-design and rapid prototyping with in-country partners. Organizers spent the fall gathering challenge statements from stakeholders in Ukraine, Taiwan, the United Kingdom, Spain, and across the United States. The goal: identify problems where a small, interdisciplinary team could make measurable progress in one month.

The participant pool reflected MIT’s open IAP structure. First-year undergraduates worked alongside senior engineers, international researchers, and Ukrainian colleagues participating remotely despite frequent blackouts. Many joined meetings from darkened apartments in Kyiv, Kharkiv, and Cherkasy — often relying on unstable heaters and backup battery packs. One participant excused himself from a design review due to an air-raid alert.

“These groups developed what I call ‘quantum entanglement,’” says Svetlana Boriskina, a principal research scientist at MIT and director of the Multifunctional Metamaterials Laboratory in the Department of Mechanical Engineering. “They were sharing data in real time across continents, while experiencing the war’s impacts directly and indirectly.”

Setting the foundation: briefings and technical overviews

The first week introduced participants to the geopolitical, technical, and humanitarian landscape that would frame their work. Topics included:

• War context and co-design practices. Boriskina and Elizabeth Wood, faculty director of the MIT-Ukraine Program and professor of history at MIT, outlined current conditions in Ukraine. Student mentor Natalie Dean ’26 (vice president of MIT’s Assistive Technology Club) led a session on co-design — emphasizing partnership with, not for, Ukrainian collaborators.
• Extreme-environment engineering. Boriskina introduced two possible technical tracks proposed by her collaborators at Kharkiv Institute of Physics and Technology: radiation-hardened materials and self-powered sensors for extreme environments, and acoustic analysis for monitoring supercritical water cooling systems in nuclear reactors. One team, later known as HotPot, adopted the latter challenge.
• AI, Open Source Intelligence, and disinformation. Phil Tinn ’16, a research scientist at SINTEF and an affiliate of the MIT-Ukraine Program, along with specialists from IN2, described how disinformation narratives travel across platforms, from Telegram to global social media. Cambridge University researcher Jon Roozenbeek discussed early threat-signal detection using pricing fluctuations in fake SMS verifications. Ukrainian partners presented on large language model bias propagation, bot detection, and media-anomaly analysis — groundwork for the eventual VibeTracking team.
• Explosive ordnance disposal. Experts from MineSight and the U.S. Army National Guard detailed the scale of landmine contamination in Ukraine — by some estimates affecting a third of the country. These sessions inspired Clearview Interface, which worked on improving visual feedback for de-mining tools.
• Drone detection. Engineers from Skyfall and MIT’s student community introduced acoustic, radiofrequency (RF), and fiber-optic-tether detection methods for drones — leading to two separate teams: Birdwatch (acoustic detection) and Hrobachki (RF detection).

Five teams, seven time zones, and one month of development

Nearly 90 people joined the project through Discord, and by the end of week one, five core teams had formed. Roles blurred: Undergraduates mentored professionals; Ukrainian engineers supplied real-time operational data; and faculty offered rapid problem-solving guidance. Each team completed a Preliminary Design Review, Critical Design Review, and final presentation to an audience of more than 80 people, online and in person.

Despite the compressed timeline, the teams delivered promising prototypes and analyses with potential real-world application.

Team highlights

Clearview Interface — Visualizing metal-detector data for safer de-mining

Two undergraduates from Olin College developed a method for converting complex metal-detector audio signals — often an overwhelming sequence of indistinguishable beeps — into intuitive visual information. Their approach could help de-miners identify object types more quickly and accurately, enhancing both safety and mapping. The team reverse-engineered commercial detector outputs and produced a preliminary interface they plan to refine this spring.

HotPot — Acoustic monitoring for nuclear-reactor cooling systems

This team of seven (five at MIT and two from the Kharkiv Institute of Physics and Technology) worked to detect transitions from water to supercritical states inside steam pipes — a critical safety parameter in nuclear facilities that have remained in operation during wartime. Combining physics simulations, hardware engineering, and acoustics, the group analyzed data from Ukrainian partners and proposed a model capable of identifying supercritical conditions via remote monitoring.

Birdwatch — Acoustic detection of fiber-optic-controlled drones

With drones frequently used along the front and often tethered to fiber-optic control lines that evade RF detection, the Birdwatch team built an audio-based detection system using a network of cameras and microphones. They trained their model on drone signatures recorded across MIT’s campus and integrated early detections into a decision-support tool to help operators interpret and act on the alerts.

Hrobachki — Radiofrequency localization for long-range drones

Two MIT students, along with collaborators at Kenyon College, Olin College, and a partner in Cherkasy, Ukraine, focused on RF detection for drones operating beyond front-line distances. They established nodes at MIT, Olin, and the town of Milton, Massachusetts, demonstrating the feasibility of distributed RF sensing for aerial threat identification.

VibeTracking — Following the movement of disinformation narratives

The smallest team — a master’s student in Lviv supported by several advisors — collaborated with IN2 to build a large-language-model pipeline that classifies and groups narratives across platforms such as Telegram and X. Their system demonstrated the likely propagation path of a specific narrative, illustrating how early-stage disinformation can be identified before it reaches mainstream channels.

Resilience, connection, and next steps

On the final day of presentations, specialists from Ukrainian universities, industry partners, and MIT-affiliated programs filled the room and populated the Zoom call. Their response was enthusiastic, not only because of what the teams produced in four weeks, but because of the collaborative networks formed under difficult conditions.

“The most important outcome is the community that emerged,” Boriskina says. “These teams built tools — but they also built relationships that will carry this work forward.”

Organizers expect several projects to continue this spring through research internships, Undergraduate Research Opportunity Program projects, and follow-on collaborations with Ukrainian institutions.

Students interested in joining ongoing Build for Ukraine projects can email the MIT-Ukraine Program. To support MIT-Ukraine initiatives, contact Svitlana Krasynska.

The Hawaiian bobtail squid has bioluminescent bacteria.

Some oil and gas companies are working to lower climate-warming emissions, even if they aren’t saying much about it.

The International Finance Corp.’s investment in a Swedish startup aims to bring down operating costs for poor countries — especially in times of high oil prices.

Interior Secretary Doug Burgum once described the carbon capture project as a “shining example of how industries can reduce emissions while investing in baseload generation.”

The Iran conflict is another reminder to diversify away from fossil fuels, the European Commission’s Teresa Ribera said during her Washington swing.