Science and Technology

Stitched for Strength: The Physics of Stiff, Knitted Fabrics

admin — Wed, 30 Jul 2025 12:39:36 +0000

Stitched for Strength: The Physics of Stiff, Knitted Fabrics admin Wed, 07/30/2025 - 08:39

School of Physics Associate Professor Elisabetta Matsumoto is unearthing the secrets of the centuries-old practice of knitting through experiments, models, and simulations. Her goal? Leveraging knitting for breakthroughs in advanced manufacturing — including more sustainable textiles, wearable electronics, and soft robotics.

Matsumoto, who is also a principal investigator at the International Institute for Sustainability with Knotted Chiral Meta Matter (WPI-SKCM2) at Hiroshima University, is the corresponding author on a new study exploring the physics of ‘jamming’ — a phenomenon when soft or stretchy materials become rigid under low stress but soften under higher tension.

The study, "Pulling Apart the Mechanisms That Lead to Jammed Knitted Fabrics," was published this week in Physical Review E, and also includes Georgia Tech Matsumoto Group graduate students Sarah Gonzalez and Alexander Cachine in addition to former postdoctoral fellow Michael Dimitriyev, who is now an assistant professor at Texas A&M University.

The work builds on the group’s previous research demonstrating that knitted materials can be mathematically ‘programmed’ to behave in predictable ways. “These properties are intuitively understood by people who knit by hand,” Matsumoto says, “but in order to manipulate and use these behaviors in an industrial setting, we need to understand the physics behind them. This new research is another step in that direction.”

An Unexpected Twist

Gonzalez, who led the research, first became interested in jamming while conducting adjacent research. “I was using model simulations to characterize how different yarn properties affect the behavior of knitted fabrics and noticed a strange stiff region,” she recalls. “In our previous research, we had also seen this behavior in lab experiments, which suggested that what we were seeing in the simulations was a genuine phenomenon. I wanted to investigate it further.”

After digging into the topic, she realized that what she was seeing was called ‘jamming.’ In knits, Gonzalez explains, jamming occurs when stitches are packed tightly together, and the fabric resists stretching. Although it’s a well-known phenomenon, the physics has mostly been investigated in granular systems, like snow or sand, rather than fabrics.

“In fabrics, when you pull softly, the response is surprisingly stiff, but when you start pulling harder and harder, the stitches rearrange, and the material softens,” Matsumoto says. “In granular systems, this is a little like how avalanches work. At low forces, the snow pack is solid, but when the slope is steep, the force of gravity liquidizes that snow pack into an avalanche.”

“In fabrics, it is a little like having a tangle in a piece of jewelry,” she adds. “If you pull on it, it gets quite stiff, but if you loosen the knot, the chain can reconfigure, and it's not so stiff.”

Unraveling the Physics of Jamming

Using a combination of experiments with industrially knitted fabrics and computer models, the team analyzed what causes jamming in fabrics and how to control it. “We wanted to determine how different yarn properties impacted jamming,” Gonzalez explains. “Our goal was to understand the mechanics of jamming through how yarn interacts at various touchpoints in stitches.”

The team found that both machine tension and yarn thickness played a key role in making a fabric more or less jammed, and that jamming behaves differently depending on which direction the fabric is stretched.

“When you stretch a knit along the rows, the stiffness of the yarn causes fabric jamming. Jamming in the other direction is due to yarn contacts,” says Gonzalez. “We also showed that the impacts of changing machine tension and yarn thickness differ depending on fabric direction.”

“Discovering that fabric jamming works differently in different directions was a key insight,” she adds. “To our knowledge, the physics of this has never been explored before.”

Modern Innovation — With a Centuries-Old Technique

The research dovetails with Matsumoto’s WPI-SKCM2 Center work, which involves investigating fundamental aspects of knots and chirality. The Center is interested in a class of materials called “knotted chiral meta matter” that could lead to more sustainable materials.

For example, knitting — which leverages chiral knots — could be used to create more elastic fabrics from natural materials. “In many cases, manufacturers use yarns that combine, for example, polyester, cotton, and elastane to create a desired elasticity,” Matsumoto says. “Our research suggests that manipulating the topology of the stitches could lead to a similar elasticity, reducing the need for petroleum-based fibers and creating a more sustainable textile.”

“Knitting has the potential to be extremely useful in manufacturing, but knowledge has typically been shared through intuition and word of mouth,” she adds. “By creating these mathematical models, we hope to formalize that knowledge in a way that’s accessible for large-scale manufacturing — so we can leverage this centuries-old intuition for modern innovation.”

Funding: This work was supported by the World Premier International Research Center Initiative (WPI), Ministry of Education, Culture, Sports, Science and Technology of Japan; National Science Foundation (NSF); and Research Corporation for Science Advancement (RCSA).

DOI: https://doi.org/10.1103/g94g-c6tt

Summary sentence

Physicists unravel the secrets of the centuries-old practice of knitting in a new study that explores the physics of ‘jamming’ — a phenomenon when soft or stretchy materials become rigid under low stress but soften under higher tension.

Summary

Researchers in the School of Physics unravel the secrets of the centuries-old practice of knitting in a new study that explores the physics of ‘jamming’ — a phenomenon when soft or stretchy materials become rigid under low stress but soften under higher tension.

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Fri, 07/25/2025 - 12:00

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Wed, 07/30/2025 - 08:38

Nuclear Power Isn’t What You Think — and That’s a Good Thing

admin — Wed, 30 Jul 2025 12:37:40 +0000

Nuclear Power Isn’t What You Think — and That’s a Good Thing admin Wed, 07/30/2025 - 08:37

“Nuclear” is a loaded, highly charged word. It can conjure images — both real and imagined — of explosive destruction.

Nuclear is also a loaded, highly charged technology. A single fuel pellet the size of a pencil eraser contains as much energy as a metric ton of coal, 150 gallons of oil, or 17,000 cubic feet of natural gas.

The technology’s complex history, along with its vast potential, is why nuclear scientists and engineers often find themselves moonlighting as myth busters. Georgia Tech experts are eager to untangle fact from fiction so nuclear can shine — safely.

“I am really excited about nuclear, but this is a technology that has a lot of myths and misinformation around it,” said Anna Erickson, Woodruff Professor in the George W. Woodruff School of Mechanical Engineering (ME), and leader of the Consortium for Enabling Technologies and Innovation (ETI), which is focused on nuclear technology.

“Concerns about nuclear weapons, accidents, and waste have overshadowed nuclear energy’s potential as a clean, carbon-free technology,” she added.

Here, Georgia Tech researchers share what nuclear is, why it’s important, and why its moment is now.

What Is Nuclear?

“Nuclear, as indicated by its name, is focused on the nucleus within an atom, but also the atom as a whole,” said Steve Biegalski, ME professor and chair of the Nuclear and Radiological Engineering and Medical Physics Program. “From an engineering perspective, we're looking at how we can use the physics of an atom — and the physics of a nucleus — to solve different scientific and societal problems.”

In 1938, German and Austrian scientists discovered that breaking apart an atom’s nucleus creates energy through fission. Many aspects of nuclear science, however, were advanced through the Manhattan Project during World War II, in which the U.S. developed the atomic bombs it later dropped on Hiroshima and Nagasaki, Japan. This historical association has likely played a significant role in shaping the negative perception of nuclear technology.

But nuclear science isn’t only about international power and weapons, Biegalski said. Advances in nuclear science have contributed to life-saving cancer therapies, cutting-edge heart scans, and on-demand X-ray technologies.

Safe levels of radiation are all around us — for example, our imported fruits and vegetables are treated with radiation when they enter the country. Even kitty litter is radioactive — not very, but detectable by modern sensors.

“You might have slightly elevated radioactivity for a short while after you eat a banana in the morning,” Erickson said. “Our bodies have evolved to live with radiation.”

AI Has Entered the Chat

Lately, Erickson has been getting calls from major technology companies with questions about how to power data centers. She isn’t surprised — nuclear energy is widely being discussed as the way to power the AI revolution.

“Today’s energy needs are very different than they were in the past, and consistent, reliable, and independent electricity production is necessary — especially for the technology sector,” Erickson explained.

“At this stage, it’s not a question of whether nuclear energy can meet those demands, but how quickly we can make it a reality,” she added.

One of nuclear’s most distinguishing features is its power density, or how much power is produced by volume of raw material. Another defining feature is its reliability. Wind and solar are weather-dependent and provide power intermittently. Nuclear can supply power around the clock, and data centers require that level of consistency.

“There are discussions about developing a number of data centers just outside of Atlanta, and those will require full-size nuclear power plants to power them,” Biegalski said. “When we look at electricity production, these facilities need power 24/7, 365 days a year. Nuclear power can supply that, and wind and solar simply cannot.”

Great Power, Great Responsibility

According to Erickson, the nuclear reactors in use today are far more advanced than those associated with past disasters like Chernobyl and Three Mile Island.

New nuclear plants are designed with great efficiency in mind. Coal must be supplied continuously, whereas nuclear can be loaded once and run for years.

In addition to dispelling misinformation, nuclear experts are also knowledgeable about nuclear nonproliferation and nuclear security. Georgia Tech is a leader in these areas. Experts like Erickson and Biegalski are regularly tapped to help design new reactors that are popping up across the country.

The Georgia Tech-led nuclear consortium, ETI, assesses how emerging technologies help or hurt nuclear nonproliferation efforts. Nuclear nonproliferation is the global effort to minimize the spread of nuclear weapons, technology, and development.

“One of our main missions is to understand expansion of civilian nuclear power through the lens of nuclear safeguards and nonproliferation,” Erickson said. “Specifically, we want to know how we can best prevent misuse and mishandling of nuclear materials and keep nuclear facilities safe, while also investing in advancing nuclear technology.”

A Shift in Public Opinion

Despite the popular culture — think Homer Simpson’s nuclear plant job handling green slime — the public is also becoming better informed about nuclear power’s relative safety, especially compared with other energy sources.

In early 2025, nearly 6 out of 10 Americans supported increased development of nuclear energy. But why are Americans gradually coming around to the idea?

Erickson may have the answer. “The technology’s potential is catching on across the globe,” she said. “In France, 70% of their electricity comes from nuclear energy.”

For one of her first research projects as a young student, Erickson analyzed what went wrong with the Chernobyl reactors. She understands why people can be wary of nuclear technology.

“Despite the uptick in support for nuclear, people still have concerns we need to answer, rather than just telling people to trust the experts,” Erickson said. “Talking to people is critical in promoting this technology and making sure we keep the public’s trust in this.”

Summary sentence

Georgia Tech researchers are advancing nuclear science and engineering while deconstructing myths around the technology.

Summary

Georgia Tech researchers are advancing nuclear science and engineering while deconstructing myths around the technology.

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Fri, 07/25/2025 - 12:00

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Wed, 07/30/2025 - 08:36

Powering the Future — Without Breaking the Grid

admin — Fri, 25 Jul 2025 20:15:41 +0000

Powering the Future — Without Breaking the Grid admin Fri, 07/25/2025 - 16:15

As Georgia positions itself as a hub for digital infrastructure, communities across the state are facing a growing challenge: how to welcome the economic benefits of data centers while managing their significant environmental and infrastructure impacts. These facilities, essential for powering artificial intelligence, cloud computing, and everyday internet use, are also among the most resource-intensive buildings in the modern economy.

While companies like Microsoft and Google have pledged to reach net-zero emissions, experts say more transparency and smarter policy are needed to ensure that data center development aligns with community and environmental priorities. That means ensuring adequate energy infrastructure, investing in renewables, training local workers, and mitigating water and carbon impacts through innovation.

A New Kind of Energy Crunch

The rapid rise of AI is fueling explosive demand for computing power — and in turn, energy.

“The proliferation of AI workloads has significantly increased data center energy requirements,” says Divya Mahajan, assistant professor in the School of Electrical and Computer Engineering. “Large-scale AI training, especially for language models, leads to elevated and sustained power draw, often nearing the thermal and power envelopes of graphics processing units systems.”

This sustained demand is particularly challenging in hot, humid regions like Georgia, where cooling systems must work harder. “Training these models can cause thermal instability that directly affects cooling efficiency and power provisioning,” Mahajan explains. “This amplifies reliance on external cooling infrastructure, increasing water consumption and grid strain.”

Environmental and Economic Pressure

“Each new data center could lead to greenhouse gas emissions equivalent to a small town,” says Marilyn Brown, Regents’ and Brook Byers Professor of Sustainable Systems in the School of Public Policy. “In Georgia, the growth of data centers has already led to plans for new gas plants and the extension of aging coal plants.”

There’s an environmental cost to this growth: electricity and water. A single large data center can consume up to 5 million gallons of water per day.

Rising demand has a price. “It’s simple supply and demand,” says Ahmed Saeed, assistant professor at the School of Computer Science. “As overall power demand increases, if supply doesn’t keep up, costs will rise and the most affected will be lower-income consumers.”

Still, experts are optimistic that policy and technology can help mitigate these impacts.

Innovation May Hold the Key

Despite the challenges, experts see opportunities for innovation. “Technologies like direct-to-chip cooling and liquid cooling are promising,” says Mahajan. “But they’re not yet widespread.”

Saeed notes that some companies are experimenting with radical ideas, like Microsoft’s underwater Project Natick or locating data centers in Nordic countries where ambient air can be used for cooling. These approaches challenge conventional infrastructure norms by placing servers underwater or in remote, cold regions. “These are exciting, but we need scalable solutions that work in places like Georgia,” he emphasizes.

What Communities Should Ask For

As communities compete to attract data centers, experts say they should push for commitments that go beyond job creation.

“Communities should ensure that their power infrastructure can handle the added load without compromising resilience or increasing costs,” Saeed advises. “They should also require that data centers use renewable energy or invest in local clean energy projects.”

Training and hiring local workers is another key benefit communities can demand. “Deployment and maintenance of data centers require skilled workers,” Saeed adds. “Operators should invest in technical training and hire locally.”

Policy Can Make the Difference

Stronger policy frameworks can ensure growth doesn’t come at the expense of Georgia’s most vulnerable communities. “We need more transparency from companies about their energy and water use,” says Brown. “And we need policies that prevent the costs of supporting large consumers from being passed on to residential ratepayers.”

Some states are already taking action. Texas passed a bill to give regulators more control over large power consumers. In Georgia, a bill that would have paused tax breaks for data centers until their community impact was assessed was vetoed — but experts say the conversation is far from over.

“Data centers are here to stay,” says Saeed. “The question is whether we can make them sustainable — before their footprint becomes too large to manage.”

Summary sentence

Georgia’s booming data center industry brings economic promise and environmental pressure. Researchers say innovation and local action can tip the balance.

Summary

As Georgia emerges as a hub for digital infrastructure, the rapid growth of data centers — driven by rising demand for AI and cloud computing — presents both economic opportunity and environmental challenges. These resource-intensive facilities strain local power grids, increase greenhouse gas emissions, and consume millions of gallons of water daily. While companies pledge sustainability goals, Georgia Tech experts say stronger policies, greater transparency, and community-driven requirements are essential to ensure that growth benefits residents without overwhelming infrastructure or raising utility costs. Innovations in energy efficiency and cooling technologies show promise, but scalable solutions tailored to Georgia’s climate are urgently needed.

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Fri, 07/25/2025 - 12:00

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Fri, 07/25/2025 - 16:15

How the World’s Nuclear Watchdog Monitors Facilities Around the World – and What it Means That Iran Kicked it Out

admin — Thu, 24 Jul 2025 17:57:00 +0000

How the World’s Nuclear Watchdog Monitors Facilities Around the World – and What it Means That Iran Kicked it Out admin Thu, 07/24/2025 - 13:57

What happens when a country seeks to develop a peaceful nuclear energy program? Every peaceful program starts with a promise not to build a nuclear weapon. Then, the global community verifies that stated intent via the Treaty on the Non-Proliferation of Nuclear Weapons.

Once a country signs the treaty, the world’s nuclear watchdog, the International Atomic Energy Agency, provides continuous and technical proof that the country’s nuclear program is peaceful.

The IAEA ensures that countries operate their programs within the limits of nonproliferation agreements: low enrichment and no reactor misuse. Part of the agreement allows the IAEA to inspect nuclear-related sites, including unannounced surprise visits.

These are not just log reviews. Inspectors know what should and should not be there. When the IAEA is not on site, cameras, tamper-revealing seals on equipment and real-time radiation monitors are working full-time to gather or verify inside information about the program’s activities.

Safeguards Toolkit

The IAEA safeguards toolkit is designed to detect proliferation activities early. Much of the work is fairly technical. The safeguards toolkit combines physical surveillance, material tracking, data analytics and scientific sampling. Inspectors are chemists, physicists and nuclear engineers. They count spent fuel rods in a cooling pond. They check tamper seals on centrifuges. Often, the inspectors walk miles through hallways and corridors carrying heavy equipment.

That’s how the world learned in April 2021 about Iran pushing uranium enrichment from reactor-fuel-grade to near-weapons-grade levels. IAEA inspectors were able to verify that Iran was feeding uranium into a series of centrifuges designed to enrich the uranium from 5%, used for energy programs, to 60%, which is a step toward the 90% level used in nuclear weapons.

Around the facilities, whether for uranium enrichment or plutonium processing, closed-circuit surveillance cameras monitor for undeclared materials or post-work activities. Seals around the facilities provide evidence that uranium gas cylinders have not been tampered with or that centrifuges operate at the declared levels. Beyond seals, online enrichment monitors allow inspectors to look inside of centrifuges for any changes in the declared enrichment process.

Seals verify whether nuclear equipment or materials have been used between onsite inspections.

When the inspectors are on-site, they collect environmental swipes: samples of nuclear materials on surfaces, in dust or in the air. These can reveal if uranium has been enriched to levels beyond those allowed by the agreement. Or if plutonium, which is not used in nuclear power plants, is being produced in a reactor. Swipes are precise. They can identify enrichment levels from a particle smaller than a speck of dust. But they take time, days or weeks. Inspectors analyze the samples at the IAEA’s laboratories using sophisticated equipment called mass spectrometers.

In addition to physical samples, IAEA inspectors look at the logs of material inventories. They look for diversion of uranium or plutonium from normal process lines, just like accountants trace the flow of finances, except that their verification is supported by the ever-watching online monitors and radiation sensors. They also count items of interest and weigh them for additional verification of the logs.

Beyond accounting for materials, IAEA inspectors verify that the facility matches the declared design. For example, if a country is expanding centrifuge halls to increase its enrichment capabilities, that’s a red flag. Changes to the layout of material processing laboratories near nuclear reactors could be a sign that the program is preparing to produce unauthorized plutonium.

Losing Access

Iran announced on June 28, 2025, that it has ended its cooperation with the IAEA. It removed the monitoring devices, including surveillance cameras, from centrifuge halls. This move followed the news by the IAEA that Iran’s enrichment activities are well outside of allowed levels. Iran now operates sophisticated uranium centrifuges, like models IR-6 and IR-9.

Removing IAEA access means that the international community loses insight into how quickly Iran’s program can accumulate weapon-grade uranium, or how much it has produced. Also lost is information about whether the facility is undergoing changes for proliferation purposes. These processes are difficult to detect with external surveillance, like satellites, alone.

An alternative to the uranium enrichment path for producing nuclear weapons material is plutonium. Plutonium can’t be mined, it has to be produced in a nuclear reactor. Iran built a reactor capable of producing plutonium, the IR-40 Heavy Water Research Reactor at the Arak Nuclear Complex.

Iran modified the Arak reactor under the now-defunct Joint Comprehensive Plan of Action to make plutonium production less likely. During the June 2025 missile attacks, Israel targeted Arak’s facilities with the aim of eliminating the possibility of plutonium production.

With IAEA access suspended, it won’t be possible to see what happens inside the facility. Can the reactor be used for plutonium production? Although a lengthier process than the uranium enrichment path, plutonium provides a parallel path to uranium enrichment for developing nuclear weapons.

Continuity of Knowledge

North Korea expelled IAEA inspectors in 2009. Within a few years, they restarted activities related to uranium enrichment and plutonium production in the Yongbyon reactor. The international community’s information about North Korea’s weapons program now relies solely on external methods: satellite images, radioactive particles like xenon – airborne fingerprints of nuclear activities – and seismic data.

What is lost is the continuity of the knowledge, a chain of verification over time. Once the seals are broken or cameras are removed, that chain is lost, and so is confidence about what is happening at the facilities.

When it comes to IAEA inspections, there is no single tool that paints the whole picture. Surveillance plus sampling plus accounting provide validation and confidence. Losing even one weakens the system in the long term.

The existing safeguards regime is meant to detect violations. The countries that sign the nonproliferation treaty know that they are always watched, and that plays a deterrence role. The inspectors can’t just resume the verification activities after some time if access is lost. Future access won’t necessarily enable inspectors to clarify what happened during the gap.

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Summary sentence

What happens when a country seeks to develop a peaceful nuclear energy program?

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What happens when a country seeks to develop a peaceful nuclear energy program?

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Sun, 07/20/2025 - 12:00

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Thu, 07/24/2025 - 13:55

Georgia Tech to Build $20M National AI Supercomputer

admin — Wed, 16 Jul 2025 18:58:41 +0000

Georgia Tech to Build $20M National AI Supercomputer admin Wed, 07/16/2025 - 14:58

The National Science Foundation (NSF) has awarded Georgia Tech and its partners $20 million to build a powerful new supercomputer that will use artificial intelligence (AI) to accelerate scientific breakthroughs.

Called Nexus, the system will be one of the most advanced AI-focused research tools in the U.S. Nexus will help scientists tackle urgent challenges such as developing new medicines, advancing clean energy, understanding how the brain works, and driving manufacturing innovations.

“Georgia Tech is proud to be one of the nation’s leading sources of the AI talent and technologies that are powering a revolution in our economy,” said Ángel Cabrera, president of Georgia Tech. “It’s fitting we’ve been selected to host this new supercomputer, which will support a new wave of AI-centered innovation across the nation. We’re grateful to the NSF, and we are excited to get to work.”

Designed from the ground up for AI, Nexus will give researchers across the country access to advanced computing tools through a simple, user-friendly interface. It will support work in many fields, including climate science, health, aerospace, and robotics.

“The Nexus system's novel approach combining support for persistent scientific services with more traditional high-performance computing will enable new science and AI workflows that will accelerate the time to scientific discovery,” said Katie Antypas, National Science Foundation director of the Office of Advanced Cyberinfrastructure. “We look forward to adding Nexus to NSF's portfolio of advanced computing capabilities for the research community.”

Nexus Supercomputer — In Simple Terms

Built for the future of science: Nexus is designed to power the most demanding AI research — from curing diseases, to understanding how the brain works, to engineering quantum materials.

Blazing fast: Nexus can crank out over 400 quadrillion operations per second — the equivalent of everyone in the world continuously performing 50 million calculations every second.

Massive brain plus memory: Nexus combines the power of AI and high-performance computing with 330 trillion bytes of memory to handle complex problems and giant datasets.

Storage: Nexus will feature 10 quadrillion bytes of flash storage, equivalent to about 10 billion reams of paper. Stacked, that’s a column reaching 500,000 km high — enough to stretch from Earth to the moon and a third of the way back.

Supercharged connections: Nexus will have lightning-fast connections to move data almost instantaneously, so researchers do not waste time waiting.

Open to U.S. researchers: Scientists from any U.S. institution can apply to use Nexus.

Why Now?

AI is rapidly changing how science is investigated. Researchers use AI to analyze massive datasets, model complex systems, and test ideas faster than ever before. But these tools require powerful computing resources that — until now — have been inaccessible to many institutions.

This is where Nexus comes in. It will make state-of-the-art AI infrastructure available to scientists all across the country, not just those at top tech hubs.

“This supercomputer will help level the playing field,” said Suresh Marru, principal investigator of the Nexus project and director of Georgia Tech’s new Center for AI in Science and Engineering (ARTISAN). “It’s designed to make powerful AI tools easier to use and available to more researchers in more places.”

Srinivas Aluru, Regents’ Professor and senior associate dean in the College of Computing, said, “With Nexus, Georgia Tech joins the league of academic supercomputing centers. This is the culmination of years of planning, including building the state-of-the-art CODA data center and Nexus’ precursor supercomputer project, HIVE."

Like Nexus, HIVE was supported by NSF funding. Both Nexus and HIVE are supported by a partnership between Georgia Tech’s research and information technology units.

A National Collaboration

Georgia Tech is building Nexus in partnership with the National Center for Supercomputing Applications at the University of Illinois Urbana-Champaign, which runs several of the country’s top academic supercomputers. The two institutions will link their systems through a new high-speed network, creating a national research infrastructure.

“Nexus is more than a supercomputer — it’s a symbol of what’s possible when leading institutions work together to advance science,” said Charles Isbell, chancellor of the University of Illinois and former dean of Georgia Tech’s College of Computing. “I'm proud that my two academic homes have partnered on this project that will move science, and society, forward.”

What’s Next

Georgia Tech will begin building Nexus this year, with its expected completion in spring 2026. Once Nexus is finished, researchers can apply for access through an NSF review process. Georgia Tech will manage the system, provide support, and reserve up to 10% of its capacity for its own campus research.

“This is a big step for Georgia Tech and for the scientific community,” said Vivek Sarkar, the John P. Imlay Dean of Computing. “Nexus will help researchers make faster progress on today’s toughest problems — and open the door to discoveries we haven’t even imagined yet.”

Subtitle

Summary sentence

Summary

The National Science Foundation has awarded Georgia Tech and its partners $20 million to build a powerful new supercomputer that will use artificial intelligence to accelerate scientific breakthroughs. Called Nexus, the system will be one of the most advanced, AI-focused research tools in the U.S. Nexus will help scientists tackle urgent challenges such as developing new medicines, advancing clean energy, understanding how the brain works, and driving manufacturing innovations.

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Tue, 07/15/2025 - 12:00

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Wed, 07/16/2025 - 14:56

LIGO Detects Most Massive Binary Black Hole to Date

admin — Wed, 16 Jul 2025 18:35:41 +0000

LIGO Detects Most Massive Binary Black Hole to Date admin Wed, 07/16/2025 - 14:35

The Laser Interferometer Gravitational-Wave Observatory (LIGO)’s LIGO-Virgo-KAGRA (LVK) collaboration has detected an extremely unusual binary black hole merger — a phenomenon that occurs when two black holes are pulled into each other's orbit and combine. Announced yesterday in a California Institute of Technology press release, the binary black hole merger, GW231123, is the largest ever detected with gravitational waves.

Before merging, both black holes were spinning exceptionally fast, and their masses fell into a range that should be very rare — or impossible.

“Most models don't predict black holes this big can be made by supernovas, and our data indicates that they were spinning at a rate close to the limit of what’s theoretically possible,” says Margaret Millhouse, a research scientist in the School of Physics who played a key role in the research. “Where could they have come from? It raises interesting questions.”

A binary black hole merger absorbs characteristics from both of the contributors, she adds. “As a result, this is not only the most massive binary black hole ever seen but also the fastest-spinning binary black hole confidently detected with gravitational waves.”

“GW231123 is a record-breaking event,” says School of Physics Professor Laura Cadonati, who has been a member of the LIGO Scientific Collaboration since 2002. “LIGO has been observing the cosmos for 10 years now. This discovery underscores that there is still so much that this instrument can help us learn.”

A Cosmic View

The findings challenge current theories on how smaller black holes form, says School of Physics Assistant Professor and LIGO collaborator Surabhi Sachdev. Smaller black holes are the result of supernovae: dying and collapsing stars. During that collapse, explosions can tear apart or eject part of the star’s mass — limiting the size of the black hole that forms.

“Black holes from supernovae can weigh up to about 60 times the mass of our Sun,” she says. “The black holes in this merger were likely the mass of hundreds of suns.”

Because of its size, GW231123 also allowed the team to study the merger in unprecedented detail. “LIGO has observed scores of black hole mergers,” says Cadonati. “Of these, GW231123 has provided us with the clearest view of the ‘grand finale’ of a merger thus far. This adds a new clue to solve the puzzle that are black holes, including their origins and properties.”

“While we saw that our expectations matched the data, the extreme nature of this event pushed our models to their limits,” Millhouse adds. “A massive, highly spinning system like this will be of interest to researchers who study how binary black holes form.”

Decoding a Split-Second Signal

Millhouse and School of Physics Postdoctoral Fellow Prathamesh Joshi used Einstein’s equations for general relativity to confirm LIGO’s detections.

To find black holes, LIGO measures distortions in spacetime — ripples that are created when two black holes collide. These patterns in gravitational waves can be used to find the signature signal of black hole collisions.

“In this case, the signal lasted for just one-tenth of a second, but it was very clear,” says Joshi. "Previously, we designed a special study to detect these interesting signals, which accounted for all the unusual properties of such massive systems — and it paid off!”

“To ensure it wasn’t noise, the Georgia Tech team first reconstructed the signal in a model-agnostic way,” Millhouse adds. “We then compared those reconstructions to a model that uses Einstein's equations of general relativity, and both reconstructions looked very similar, which helped confirm that this highly unusual phenomenon was a genuine detection.”

Sachdev says that seeing the signal at both LIGO Observatories — placed in Hanford, Washington and Livingston, Louisiana — was also critical. “These short signals are very hard to detect, and this signal is so unlike any of the other binary black holes that we've seen before,” she says. “Without both detectors, we would have missed it.”

A Decade of Discovery

While the team has yet to determine how the original black holes formed, one theory is that they may have resulted from mergers themselves. “This could have been a chain of mergers,” Sachdev explains. “This tells us that they could have existed in a very dense environment like a nuclear star cluster or an active galactic nucleus.” Their spins provide another clue as spinning is a characteristic usually seen in black holes resulting from a merge.

The team adds that GW231123 could provide clues on how larger black holes are formed — including the mysterious supermassive black holes at the center of galaxies.

“Gravitational wave science is almost a decade old, and we're still making fundamental discoveries,” says Millhouse. “It’s exciting that LIGO is continuing to detect new phenomena, and this is at the edge of what we've seen thus far. There's still so much we can learn.”

The team expects to update their catalogue of black holes in August 2025, which will provide another window into how this exceptionally heavy black hole might fit into the universe, and what we can continue to learn from it.

Funding: The LIGO Laboratory is supported by the U.S. National Science Foundation and operated jointly by Caltech and MIT.

Summary sentence

Before merging, both black holes were spinning exceptionally fast, and their masses fell into a range that should be very rare — or impossible.

Summary

Before merging, both black holes were spinning exceptionally fast, and their masses fell into a range that should be very rare — or impossible. The result of the merge, GW231123, is the largest binary black hole merger ever detected with gravitational waves.

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Tue, 07/15/2025 - 12:00

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Wed, 07/16/2025 - 14:35

Georgia Tech to Build $20M National AI Supercomputer

admin — Tue, 15 Jul 2025 15:05:37 +0000

Georgia Tech to Build $20M National AI Supercomputer admin Tue, 07/15/2025 - 11:05

Called Nexus, the system will be one of the most advanced, AI-focused research tools in the U.S. Nexus will help scientists tackle urgent challenges such as developing new medicines, advancing clean energy, understanding how the brain works, and driving manufacturing innovations.

Nexus Supercomputer — In Simple Terms

Built for the future of science: Nexus is designed to power the most demanding AI research — from curing diseases to understanding how the brain works to engineering quantum materials.

Blazing fast: Nexus can crank out over 400 quadrillion operations per second — the equivalent of everyone in the world continuously performing 50 million calculations every second.

Massive brain + memory: Nexus combines the power of AI and high-performance computing, with 330 trillion bytes of memory to handle complex problems and giant datasets.

Storage: Nexus will feature 10 quadrillion bytes of flash storage, equivalent to about 10 billion reams of paper. Stacked, that’s a column reaching 500,000 km high — enough to stretch from Earth to the Moon and a third of the way back.

Supercharged connections: Nexus will have lightning-fast connections to move data almost instantaneously, so researchers do not waste time waiting.

Open to U.S. researchers: Scientists from any U.S. institution can apply to use Nexus.

Why Now?

This is where Nexus comes in. It will make state-of-the-art AI infrastructure available to scientists all across the country, not just those at top tech hubs.

Like Nexus, HIVE was supported by NSF funding. Both Nexus and the HIVE are supported by a partnership between Georgia Tech’s research and information technology units.

A National Collaboration

Georgia Tech is building Nexus in partnership with the National Center for Supercomputing Applications (NCSA) at the University of Illinois Urbana-Champaign, which runs several of the country’s top academic supercomputers. The two institutions will link their systems through a new high-speed network, creating a national research infrastructure.

Tech companies, whose technologies will power the system, will also play a role.

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Tue, 07/15/2025 - 11:05

Georgia Tech to Build $20M National AI Supercomputer

admin — Tue, 15 Jul 2025 14:11:41 +0000

Georgia Tech to Build $20M National AI Supercomputer admin Tue, 07/15/2025 - 10:11

Nexus Supercomputer — In Simple Terms

Built for the future of science: Nexus is designed to power the most demanding AI research — from curing diseases, to understanding how the brain works, to engineering quantum materials.

Blazing fast: Nexus can crank out over 400 quadrillion operations per second — the equivalent of everyone in the world continuously performing 50 million calculations every second.

Massive brain plus memory: Nexus combines the power of AI and high-performance computing with 330 trillion bytes of memory to handle complex problems and giant datasets.

Storage: Nexus will feature 10 quadrillion bytes of flash storage, equivalent to about 10 billion reams of paper. Stacked, that’s a column reaching 500,000 km high — enough to stretch from Earth to the moon and a third of the way back.

Supercharged connections: Nexus will have lightning-fast connections to move data almost instantaneously, so researchers do not waste time waiting.

Open to U.S. researchers: Scientists from any U.S. institution can apply to use Nexus.

Why Now?

This is where Nexus comes in. It will make state-of-the-art AI infrastructure available to scientists all across the country, not just those at top tech hubs.

Like Nexus, HIVE was supported by NSF funding. Both Nexus and HIVE are supported by a partnership between Georgia Tech’s research and information technology units.

A National Collaboration

What’s Next

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Tue, 07/15/2025 - 12:00

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Tue, 07/15/2025 - 10:10

AI and Art Collide in This Engineering Course That Puts Human Creativity First

admin — Mon, 14 Jul 2025 18:24:41 +0000

AI and Art Collide in This Engineering Course That Puts Human Creativity First admin Mon, 07/14/2025 - 14:24

Uncommon Courses is an occasional series from The Conversation U.S. highlighting unconventional approaches to teaching.

Title of Course:

Art and Generative AI

What Prompted the Idea for the Course?

I see many students viewing artificial intelligence as humanlike simply because it can write essays, do complex math or answer questions. AI can mimic human behavior but lacks meaningful engagement with the world. This disconnect inspired the course and was shaped by the ideas of 20th-century German philosopher Martin Heidegger. His work highlights how we are deeply connected and present in the world. We find meaning through action, care and relationships. Human creativity and mastery come from this intuitive connection with the world. Modern AI, by contrast, simulates intelligence by processing symbols and patterns without understanding or care.

In this course, we reject the illusion that machines fully master everything and put student expression first. In doing so, we value uncertainty, mistakes and imperfection as essential to the creative process.

This vision expands beyond the classroom. In the 2025-26 academic year, the course will include a new community-based learning collaboration with Atlanta’s art communities. Local artists will co-teach with me to integrate artistic practice and AI.

The course builds on my 2018 class, Art and Geometry, which I co-taught with local artists. The course explored Picasso’s cubism, which depicted reality as fractured from multiple perspectives; it also looked at Einstein’s relativity, the idea that time and space are not absolute and distinct but part of the same fabric.

What Does the Course Explore?

We begin with exploring the first mathematical model of a neuron, the perceptron. Then, we study the Hopfield network, which mimics how our brain can remember a song from just listening to a few notes by filling in the rest. Next, we look at Hinton’s Boltzmann Machine, a generative model that can also imagine and create new, similar songs. Finally, we study today’s deep neural networks and transformers, AI models that mimic how the brain learns to recognize images, speech or text. Transformers are especially well suited for understanding sentences and conversations, and they power technologies such as ChatGPT.

In addition to AI, we integrate artistic practice into the coursework. This approach broadens students’ perspectives on science and engineering through the lens of an artist. The first offering of the course in spring 2025 was co-taught with Mark Leibert, an artist and professor of the practice at Georgia Tech. His expertise is in art, AI and digital technologies. He taught students fundamentals of various artistic media, including charcoal drawing and oil painting. Students used these principles to create art using AI ethically and creatively. They critically examined the source of training data and ensured that their work respects authorship and originality.

Students also learn to record brain activity using electroencephalography – EEG – headsets. Through AI models, they then learn to transform neural signals into music, images and storytelling. This work inspired performances where dancers improvised in response to AI-generated music.

The Improv AI performance at Georgia Tech on April 15, 2025. Dancers improvised to music generated by AI from brain waves and sonified black hole data.

Why is This Course Relevant Now?

AI entered our lives so rapidly that many people don’t fully grasp how it works, why it works, when it fails or what its mission is.

In creating this course, the aim is to empower students by filling that gap. Whether they are new to AI or not, the goal is to make its inner algorithms clear, approachable and honest. We focus on what these tools actually do and how they can go wrong.

We place students and their creativity first. We reject the illusion of a perfect machine, but we provoke the AI algorithm to confuse and hallucinate, when it generates inaccurate or nonsensical responses. To do so, we deliberately use a small dataset, reduce the model size or limit training. It’s in these flawed states of AI that students step in as conscious co-creators. The students are the missing algorithm that takes back control of the creative process. Their creations do not obey AI but reimagine it by the human hand. The artwork is rescued from automation.

What’s a Critical Lesson From the Course?

Students learn to recognize AI’s limitations and harness its failures to reclaim creative authorship. The artwork isn’t generated by AI, but it’s reimagined by students.

Students learn chatbot queries have an environmental cost because large AI models use a lot of power. They avoid unnecessary iterations when designing prompts or using AI. This helps reducing carbon emissions.

The Improv AI performance on April 15, 2025, featured dancer Bekah Crosby responding to AI-generated music from brain waves.

What Will the Course Prepare Students to Do?

The course prepares students to think like artists. Through abstraction and imagination they gain the confidence to tackle the engineering challenges of the 21st century. These include protecting the environment, building resilient cities and improving health.

Students also realize that while AI has vast engineering and scientific applications, ethical implementation is crucial. Understanding the type and quality of training data that AI uses is essential. Without it, AI systems risk producing biased or flawed predictions.

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Summary sentence

Uncommon Courses is an occasional series from The Conversation U.S. highlighting unconventional approaches to teaching.

Summary

Uncommon Courses is an occasional series from The Conversation U.S. highlighting unconventional approaches to teaching.

Dateline

Wed, 07/09/2025 - 12:00

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Francesco Fedele, associate professor of Civil and Environmental Engineering, Georgia Institute of Technology

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Shelley Wunder-Smith
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Mon, 07/14/2025 - 14:24

AI in Healthcare Could Save Lives and Money — But Change Won’t Happen Overnight

admin — Mon, 14 Jul 2025 16:00:41 +0000

AI in Healthcare Could Save Lives and Money — But Change Won’t Happen Overnight admin Mon, 07/14/2025 - 12:00

Imagine walking into your doctor’s office feeling sick – and rather than flipping through pages of your medical history or running tests that take days, your doctor instantly pulls together data from your health records, genetic profile and wearable devices to help decipher what’s wrong.

This kind of rapid diagnosis is one of the big promises of artificial intelligence for use in health care. Proponents of the technology say that over the coming decades, AI has the potential to save hundreds of thousands, even millions of lives.

What’s more, a 2023 study found that if the health care industry significantly increased its use of AI, up to US$360 billion annually could be saved.

But though artificial intelligence has become nearly ubiquitous, from smartphones to chatbots to self-driving cars, its impact on health care so far has been relatively low.

A 2024 American Medical Association survey found that 66% of U.S. physicians had used AI tools in some capacity, up from 38% in 2023. But most of it was for administrative or low-risk support. And although 43% of U.S. health care organizations had added or expanded AI use in 2024, many implementations are still exploratory, particularly when it comes to medical decisions and diagnoses.

I’m a professor and researcher who studies AI and health care analytics. I’ll try to explain why AI’s growth will be gradual, and how technical limitations and ethical concerns stand in the way of AI’s widespread adoption by the medical industry.

Inaccurate Diagnoses, Racial Bias

Artificial intelligence excels at finding patterns in large sets of data. In medicine, these patterns could signal early signs of disease that a human physician might overlook – or indicate the best treatment option, based on how other patients with similar symptoms and backgrounds responded. Ultimately, this will lead to faster, more accurate diagnoses and more personalized care.

AI can also help hospitals run more efficiently by analyzing workflows, predicting staffing needs and scheduling surgeries so that precious resources, such as operating rooms, are used most effectively. By streamlining tasks that take hours of human effort, AI can let health care professionals focus more on direct patient care.

But for all its power, AI can make mistakes. Although these systems are trained on data from real patients, they can struggle when encountering something unusual, or when data doesn’t perfectly match the patient in front of them.

As a result, AI doesn’t always give an accurate diagnosis. This problem is called algorithmic drift – when AI systems perform well in controlled settings but lose accuracy in real-world situations.

Racial and ethnic bias is another issue. If data includes bias because it doesn’t include enough patients of certain racial or ethnic groups, then AI might give inaccurate recommendations for them, leading to misdiagnoses. Some evidence suggests this has already happened.

Humans and AI are beginning to work together at this Florida hospital.

Data-Sharing Concerns, Unrealistic Expectations

Health care systems are labyrinthian in their complexity. The prospect of integrating artificial intelligence into existing workflows is daunting; introducing a new technology like AI disrupts daily routines. Staff will need extra training to use AI tools effectively. Many hospitals, clinics and doctor’s offices simply don’t have the time, personnel, money or will to implement AI.

Also, many cutting-edge AI systems operate as opaque “black boxes.” They churn out recommendations, but even its developers might struggle to fully explain how. This opacity clashes with the needs of medicine, where decisions demand justification.

But developers are often reluctant to disclose their proprietary algorithms or data sources, both to protect intellectual property and because the complexity can be hard to distill. The lack of transparency feeds skepticism among practitioners, which then slows regulatory approval and erodes trust in AI outputs. Many experts argue that transparency is not just an ethical nicety but a practical necessity for adoption in health care settings.

There are also privacy concerns; data sharing could threaten patient confidentiality. To train algorithms or make predictions, medical AI systems often require huge amounts of patient data. If not handled properly, AI could expose sensitive health information, whether through data breaches or unintended use of patient records.

For instance, a clinician using a cloud-based AI assistant to draft a note must ensure no unauthorized party can access that patient’s data. U.S. regulations such as the HIPAA law impose strict rules on health data sharing, which means AI developers need robust safeguards.

Privacy concerns also extend to patients’ trust: If people fear their medical data might be misused by an algorithm, they may be less forthcoming or even refuse AI-guided care.

The grand promise of AI is a formidable barrier in itself. Expectations are tremendous. AI is often portrayed as a magical solution that can diagnose any disease and revolutionize the health care industry overnight. Unrealistic assumptions like that often lead to disappointment. AI may not immediately deliver on its promises.

Finally, developing an AI system that works well involves a lot of trial and error. AI systems must go through rigorous testing to make certain they’re safe and effective. This takes years, and even after a system is approved, adjustments may be needed as it encounters new types of data and real-world situations.

AI could rapidly accelerate the discovery of new medications.

Incremental Change

Today, hospitals are rapidly adopting AI scribes that listen during patient visits and automatically draft clinical notes, reducing paperwork and letting physicians spend more time with patients. Surveys show over 20% of physicians now use AI for writing progress notes or discharge summaries. AI is also becoming a quiet force in administrative work. Hospitals deploy AI chatbots to handle appointment scheduling, triage common patient questions and translate languages in real time.

Clinical uses of AI exist but are more limited. At some hospitals, AI is a second eye for radiologists looking for early signs of disease. But physicians are still reluctant to hand decisions over to machines; only about 12% of them currently rely on AI for diagnostic help.

Suffice to say that health care’s transition to AI will be incremental. Emerging technologies need time to mature, and the short-term needs of health care still outweigh long-term gains. In the meantime, AI’s potential to treat millions and save trillions awaits.

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Summary sentence

Though artificial intelligence has become nearly ubiquitous, from smartphones to chatbots to self-driving cars, its impact on health care so far has been relatively low.

Summary

Though artificial intelligence has become nearly ubiquitous, from smartphones to chatbots to self-driving cars, its impact on health care so far has been relatively low.

Dateline

Fri, 07/11/2025 - 12:00

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Author:

Turgay Ayer, professor of Industrial and Systems Engineering, Georgia Institute of Technology

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Shelley Wunder-Smith
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Mon, 07/14/2025 - 11:59

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Nuclear Power Isn’t What You Think — and That’s a Good Thing

What Is Nuclear?

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Georgia Tech to Build $20M National AI Supercomputer

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LIGO Detects Most Massive Binary Black Hole to Date

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Georgia Tech to Build $20M National AI Supercomputer

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Georgia Tech to Build $20M National AI Supercomputer

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AI and Art Collide in This Engineering Course That Puts Human Creativity First

Title of Course:

What Prompted the Idea for the Course?

What Does the Course Explore?

Why is This Course Relevant Now?

What’s a Critical Lesson From the Course?

What Will the Course Prepare Students to Do?

Author:

Media Contact:

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AI in Healthcare Could Save Lives and Money — But Change Won’t Happen Overnight

Inaccurate Diagnoses, Racial Bias

Data-Sharing Concerns, Unrealistic Expectations

Incremental Change

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