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KISTI Supercomputer Hangang Delivers World‑Class Performance for Korean Science
Ranks around 12th worldwide in performance, full operation starts in December
New metabolic score may help predict healthy aging and disease risk
Studies conducted in various countries indicate that approximately 60% of people over the age of 65 currently live with two or more chronic conditions simultaneously.
Decommissioned NVIDIA supercomputer with 9,600 A100 GPUs hits eBay for $60 million, free shipping included
A used NVIDIA AI cluster containing 9,600 A100 GPUs and 4,200 AMD EPYC CPUs appeared on eBay. Its $59.7 million asking price included free ground delivery.
Supercomputer reveals first two billion years of ‘cosmic web’
The Center for Astrophysics’s simulation offers the most detailed look yet at the infancy of the universe after the Big Bang
Argonne welcomes Chicagoland mayors to campus for meeting and tours of user facilities
Argonne hosted the Metropolitan Mayors Caucus for its quarterly meeting, where dozens of Chicagoland leaders learned about the lab’s work in super intelligence and resilience planning and toured the Advanced Photon Source and Aurora supercomputer.
A supercomputer is about to launch to the moon for the 1st time, aboard a rover named FLIP
And yes, it has AI.
Companies pledge free software and computing for AI in science
The European Commission has announced that a group of companies and foundations will offer free services, such as software or access to supercomputers, to European scientists using AI in their research.Unveiled on October 15 at a conference in Dublin, the initiative is the latest step by the Commission to try to help researchers use the technology. Scientists have used forms of AI, such as machine learning, for decades. But advances in deep learning in the past decade, and the wider interest in large language models since the release of ChatGPT in 2022, have spurred governments to invest in new AI tools that could help the scientific process.In July, the US announced it is spending $5 billion on the Genesis Mission, which it describes as a “national effort to harness AI for science,” funding 278 projects across areas such as autonomous labs or new AI tools in astronomy. The Commission has also identified AI in science as a priority, but the €107 million it pledged from Horizon Europe in 2025 does not come close to US spending levels. That said, the vast majority of public research spending in the EU comes from national funds. Today’s announcement at the European AI in Science Summit doesn’t commit any new public money. Instead, it launches the AI in Science Enablers Initiative, a set of voluntary contributions from companies and foundations. The idea is that the companies also benefit by getting access to scientific ecosystems, and feedback on how their tools work. Google, for example, has put up €3 million of cloud computing credits to European researchers. The Novo Nordisk Foundation intends to make its supercomputer, Gefion, available to European researchers, according to the Commission statement. The chipmaker Nvidia said it would provide access to tools, compute, training and collaborative projects.The publisher Elsevier also offered complementary licenses to some of its AI tools, plus AI training for PhD, postdoctoral and master’s students. The French AI firm Mistral will provide the European research community with access to its open-weight models, Vibe seats and application programming interface credits, according to the Commission’s statement. Vibe is one of the firm’s chatbot models. The idea is that more firms and foundations will come on board in the future as the initiative grows.
Samuel Williams Honored with E. O. Lawrence Award
Awarded for creating transformative performance modeling tools and algorithms that help supercomputers run more efficiently.
AI-powered home monitoring tracks older adults’ health while protecting privacy
Older people could be monitored safely in their own homes without the need for intrusive cameras or wearable devices thanks to new research by Nottingham Trent University (NTU).
The Milky Way is one galaxy — but it used to be thousands, new simulations reveal
A new supercomputer simulation traces how thousands of small galaxies eventually became the Milky Way.
EU launches network to harness health data and AI for cardiovascular care
A new EU-funded flagship initiative to harness the power of health data and artificial intelligence (AI) across the continuum of cardiovascular care has been launched, with funding from the EU4Health Program.
ASUS Announces New 64GB Configuration of the Ascent GX10
ASUS today announced a new 64 GB configuration of ASUS Ascent GX10, expanding the lineup alongside the existing 128 GB model. This compact AI supercomputer is designed to bring local agentic AI development within reach of more developers, researchers and data scientists. Based on the NVIDIA DGX...
NVIDIA’s 64 GB DGX Spark “AI Supercomputer” Launches This Month For $4999, While The 128 GB Spark Jumps Past $6000
NVIDIA is launching a new version of its DGX Spark AI Supercomputer which packs half the memory, but is priced the same as the 128 GB model.
SDSC Helps Improve AI-Ready Geospatial Data Through GeoCroissant
Oct. 1, 2026 — Researchers working with Earth science data often face a basic but time-consuming challenge: Determining what datasets exist, what they contain and whether they can be used together. Doug Fils, with the University of California San Diego Halıcıoğlu School of Data Science and Computing’s San Diego Supercomputer Center (SDSC), is helping advance a […]
IEDC boasts businesses moving to Indiana, eyes supercomputer and Space Academy
State officials are touting strong wage growth and low unemployment while attracting business investment to Indiana. Supercomputer and Space Academy are being eyed as future investments.
LRZ Hosts TPC Europe Hackathon on AI for Science and Supercomputing
Agentic AI is currently a hot topic in the scientific community: during the Trillion Parameter Consortium’s autumn hackathon at the LRZ, teams presented practical solutions designed to make supercomputers more accessible and research projects more efficient. Sept. 24, 2026 — In the seminar rooms of the Leibniz Supercomputing Centre (LRZ), small groups stand in front […]
LRZ Hosts TPC Europe Hackathon on AI for Science and Supercomputing
Agentic AI is currently a hot topic in the scientific community: during the Trillion Parameter Consortium’s autumn hackathon at the LRZ, teams presented practical solutions designed to make supercomputers more accessible and research projects more efficient. Sept. 24, 2026 — In the seminar rooms of the Leibniz Supercomputing Centre (LRZ), small groups stand in front […]
Supercomputer predicts if Juventus will make the top four
Juventus failed to finish in the top four at the end of last season, and the Bianconeri cannot afford to suffer the same fate again this campaign. That is why the club worked so hard during the summer...
Your weather app runs on satellites, supercomputers and a retired science teacher who walked to his rain gauge at 7 a.m. every day for 50 years
The forecast on your phone has a secret ingredient: 8,700 volunteers with a thermometer. Meet the Frenches, a family in Gouverneur, N.Y. Ray monitored the stats for 50 years before his son took over. The 11-year-old granddaughter is next.
Supercomputer predicts title race after Brighton beat Arsenal
Arsenal remain among the favourites to win the Premier League title at the end of this season, and the Gunners have maintained a strong standing among fans and neutrals despite their defeat to Brighto...
New framework outlines principles for health-literate artificial intelligence
Artificial intelligence is rapidly becoming part of how people seek, interpret, and act on health information.
Experts examine ethical risks of using AI in public health
Artificial intelligence (AI) holds profound potential to reshape public health. Although its application accelerates threat detection and targeted interventions, the rapid deployment has outpaced the regulatory, validation, and equity safeguards standard to traditional health interventions.
Supercomputer predicts Chelsea vs Leeds United in Carabao Cup
Xabi Alonso looking straight with concerned Daniel Farke ahead of Stamford Bridge background
Nvidia wants to turn your house of gaming PCs into an AI supercomputer
Nvidia's free RTX PAIR app spreads local AI workloads across multiple GPUs on your network, dramatically cutting down on processing times.
Scientist says only one thing truly exists: consciousness
Have you ever stopped to think about what your brain is actually doing right now? For decades, standard science has told us a pretty straightforward story: your brain is a biological supercomputer, an...
Scientists Release Biggest 2D Map of the Universe
Key Takeaways The DESI Legacy Imaging Surveys combined more than 263,000 telescope exposures to make the largest 2D map of the universe in visible and near-infrared light. Astronomers can pair the Legacy Surveys map with their own observations to explore our universe and search for rare phenomena. The 2D map serves as the foundation for the Dark Energy Spectroscopic Instrument survey to measure the universe in 3D and investigate dark energy. Hold on to your telescopes: the DESI Legacy Imaging Surveys team has released the largest-ever 2D color map of the universe. The 5.6-trillion-pixel map contains nearly 4 billion celestial objects, primarily stars and galaxies. The data is available for all to use and publicly viewable through the Legacy Survey Sky Viewer. Astronomers and citizen scientists can explore the map or combine it with their own observations to better understand our universe. Researchers can search for rare phenomena like gravitational lenses, observe fleeting events like supernovae, and investigate two of physics’ biggest mysteries: dark matter, the invisible substance that accounts for most of the mass in our universe, and dark energy, the force driving our universe’s accelerating expansion. The new map builds on earlier versions from the DESI Legacy Imaging Surveys that have already proved invaluable. To date, more than 1,800 science papers that reference the Legacy Surveys data have been published. “It’s part of the fabric of astronomy research now,” said David Schlegel, a co-lead of the Legacy Surveys and scientist at the Department of Energy’s Lawrence Berkeley National Laboratory (Berkeley Lab). “When you’re working with astronomical objects today, you often start by pulling up the Legacy Imaging Viewer to see what you’re looking at.” Covering roughly 75% of the sky in visible and near-infrared light, the updated map provides a deep view of the extragalactic universe not blocked by the dust and stars of our own Milky Way. Researchers expect it will remain the most comprehensive 2D map of our universe for years to come. More than 160 scientists contributed to data collection for the project, and a team of 20 produced the final dataset released today. It was built by combining 263,407 telescope exposures from three ground-based sky surveys: the Dark Energy Camera Legacy Survey (DECaLS) at NSF Cerro Tololo Inter-American Observatory, the Mayall z-band Legacy Survey (MzLS) at NSF Kitt Peak National Observatory, and the Beijing-Arizona Sky Survey (BASS) at the University of Arizona’s Steward Observatory. That was supplemented by years of data from NASA’s Wide-field Infrared Survey Explorer (WISE) satellite mission and additional public data. “For our team, these data are fundamental to our investigation of the expansion history of the universe and the formation of our galaxy,” said Arjun Dey, co-lead of the Legacy Surveys and an astronomer at NSF NOIRLab. “But the skies belong to everyone, and this survey gives everyone the chance to explore the sky and marvel at its wonders.” Here be galaxies The DESI Legacy Imaging Surveys were originally conducted to prepare for the Dark Energy Spectroscopic Instrument (DESI) survey. The Legacy Surveys’ 2D map is essentially a deep photograph of the sky; it records where galaxies and stars appear and how bright they appear. This crucial step enables DESI to select objects and measure their light in different wavelengths to determine their distances, building the largest high-resolution 3D map ever made. Scientists study the way galaxies have clustered at different ages of the universe to track dark energy over time. In April 2026, DESI completed its original five-year survey ahead of schedule and with vastly more objects than expected. The early results have shown surprising hints that dark energy’s impact may be weakening over time — a paradigm shift that could potentially shape the predicted fate of our universe. DESI expects to publish improved results using their first five years of data in 2027 and is continuing observations into 2028. DESI was so efficient at observing galaxies, the Legacy Surveys map needed to expand. Early on, “it became clear we might run out of galaxies to look at and run out of sky, and we better start doing something about that,” said Schlegel, who also works on DESI. The new Legacy Surveys map has been used to select DESI targets since June 2026 and will guide the telescope’s operations over the coming years. Computing the cosmos Merging hundreds of thousands of images taken on 2,285 nights, each with unique atmospheric and telescope conditions, was a massive computational effort. It took about a year to develop the computer code and eight weeks to process all the images at the Perlmutter supercomputer at the National Energy Research Scientific Computing Center (NERSC) at Berkeley Lab. Beyond supporting DESI, the Legacy Surveys will be a foundational reference for the next generation of telescopes. As new observatories like the NSF-DOE Vera C. Rubin Observatory (jointly funded by NSF and DOE’s Office of Science) and NASA’s Nancy Grace Roman Space Telescope come online, researchers can compare their observations with one of the deepest and most comprehensive views of the sky ever assembled. The Legacy Surveys data will also help scientists train artificial intelligence tools to analyze petabytes of astronomical data and accelerate new discoveries. It will be among the datasets used in an astrophysics pilot project within the American Science Cloud, part of the DOE’s Genesis Mission. The DESI Legacy Imaging Surveys are supported by the U.S. Department of Energy’s Office of High Energy Physics; the National Energy Research Scientific Computing Center, a DOE Office of Science user facility; the U.S. National Science Foundation, Division of Astronomical Sciences; and the partner institutions. DESI is supported by the DOE Office of Science and NERSC. Additional support for DESI is provided by the NSF; the Science and Technology Facilities Council of the United Kingdom; the Gordon and Betty Moore Foundation; the Heising-Simons Foundation; the French Alternative Energies and Atomic Energy Commission (CEA); the Secretariat of Science, Humanities, Technology and Innovation (SECIHTI) of Mexico; the Ministry of Science and Innovation of Spain; and by the DESI member institutions. ### Lawrence Berkeley National Laboratory (Berkeley Lab) is committed to groundbreaking research focused on discovery science and solutions for abundant and reliable energy supplies. The lab’s expertise spans materials, chemistry, physics, biology, earth and environmental science, mathematics, and computing. Researchers from around the world rely on the lab’s world-class scientific facilities for their own pioneering research. Founded in 1931 on the belief that the biggest problems are best addressed by teams, Berkeley Lab and its scientists have been recognized with 17 Nobel Prizes. Berkeley Lab is a multiprogram national laboratory managed by the University of California for the U.S. Department of Energy’s Office of Science. DOE’s Office of Science is the single largest supporter of basic research in the physical sciences in the United States, and is working to address some of the most pressing challenges of our time. For more information, please visit energy.gov/science.
The IAC and California-based Predictive Science Inc Predict the Solar Corona for the August 12 Eclipse Using the La Palma Supercomputer
Ahead of the upcoming solar eclipse on August 12, the Instituto de Astrofísica de Canarias (IAC), in collaboration with the research group Predictive Science Inc. (PSI, San Diego, California), has successfully simulated in high detail the structure of the solar corona that will be visible during totality along the path of totality, including Palencia, where the IAC will gather more than 40 spanish, moroccan, and american astronomers.
Breaking through the quantum noise barrier
Quantum computing has spent years existing in the space between promise and practicality.The machines exist. Tech giants are building them. Researchers have shown that, for certain problems, quantum computers could one day dramatically outperform today’s fastest supercomputers.The obstacle isn’t that the computers don’t work. It’s that they’re extraordinarily fragile.
AI may predict harmful chemical exposures affecting human health
Artificial intelligence is rapidly improving scientists' ability to detect chemicals in the environment and human body.
Opening the future of science
The U.S. NSF LCCF Overview and Characteristic Science Applications virtual panel featured remarks from NSF and TACC leaders, followed by a moderated panel of researchers using TACC's Frontera and Vista supercomputers to tackle some of science's most complex challenges.
How should European scientists respond to the US AI kill switch?
Earlier in June, Europe’s worst fears of technological dependence were confirmed when the White House ordered the AI firm Anthropic to stop non-US citizens using its leading models. This kill-switch moment crystalised anxieties in Brussels that the continent, lagging the US on the creation of leading-edge AI models, is now a technological of vassal of Washington. The shock has specific implications for European scientists and universities. Some governments are now worried that with AI being integrated into labs, Donald Trump now has his finger hovering over an off switch for European science. “Having restrictions on this tool, it can have an impact on our own research,” said France’s research minister, Philippe Baptiste, at the VivaTech conference in Paris on June 18.Baptiste, himself a former academic, said that colleagues in mathematics had recently warned him they were now cut off from leading-edge US models, risking their work. “This model is not available anymore now because of the US restriction, and for the first time this had an impact on mathematics,” a field where large language models (LLMs) are making a significant contribution, said Baptiste. So, should European scientists avoid relying on US models in the future? Or would this be a self-harming overreaction? And how is AI being used in science, anyway? What happened?On June 12, it emerged that Washington, citing cybersecurity concerns, had ordered Anthropic to stop non-US citizens using two models: Claude Mythos 5, and Claude Fable 5, a version of Mythos but with guardrails to prevent abuse. Anthropic said Washington’s concerns were overblown. But forced to follow the order, yet with no way to check who was using the models, the company suddenly shut down access for everyone, US citizens included. There’s still no agreement on how to restore broad access. But last week, Bloomberg reported that select US companies, and the US government, still had access to Mythos despite the ban, but the EU’s cybersecurity agency Enisa had been shut out. Class division of researchersIn the longer term, access outside the US should be restored, said Marcel Salathé, co-director of the AI centre at the Federal Polytechnic School of Lausanne (EPFL), because a permanent restriction would be financially ruinous to US AI firms. “Even Trump will eventually budge to economic pressure,” he said.However, US AI firms might end up operating rather like banks, doing due diligence on users of their leading-edge AI models, he went on.European scientists are unlikely to fall foul of such checks, he said. But having a Chinese postgraduate in their lab, or doing a type of research the US disapproved of, could potentially result in access being denied. “It will create a sort of class division of researchers,” he said. More than frontier modelsHowever, successfully deploying AI in science is about far more than just having access to leading LLMs such as Mythos.Instead, many science-specific tools, most famously Alphafold, which predicts protein folding structures, require “far less” computing power to train than frontier models, well within the capabilities of European supercomputers, according to a recent analysis by the Arq Foundation, a Brussel-based AI think tank. Instead, tools such as Alphafold are possible because certain fields, like protein folding, weather prediction or astronomy, have large, clean, standardised datasets that can be used to train predictive models. Rather than compute, “data is the central constraint for AI for science,” the report says. For example, a less powerful physics model trained on CERN’s data “will be more valuable than the best model without certain data,” said Bengüsu Özcan, author of the report. European advantage?Europe could therefore steal a march by using its scientific data and infrastructure to create models US digital giants cannot. “They might actually match or go even beyond what these big [US] companies are doing,” said Özcan.Indeed, some European start-ups hope to create automated testing labs to create the kind of standardised data that could speed up the discovery of new materials. Materials scientists say there’s currently too little data to train models. AI scientistsHowever, even if the bottleneck for AI in science is often data, rather than compute, being at the mercy of the US for LLM access could still become a problem for European scientists. A new class of “AI scientists” based on leading LLMs is also emerging, Özcan’s report says.Unlike domain-specific tools like Alphafold, these try to automate day-to-day scientific work, by providing an overarching tool that coordinates specific models to synthesise existing literature, generate hypotheses or run analysis of data. The problem for Europe, however, is that these tools are sometimes based on US leading models. Google Deepmind’s AI Co-Scientist, for example, is built on Google’s Gemini LLM. Systematic failureThese scientific automation tools are still in their early stages, and still exhibit “systematic failures on scientific tasks,” Özcan’s report says. One of the problems is that the messy, day-to-day process of scientific discovery is largely not documented, meaning LLMs don’t have a good source of data to learn how scientists actually work. Meanwhile, some tasks, such as literature synthesis, don’t typically require access to leading edge models.However, the performance of AI scientific assistants could improve as companies such as Google pour resources into developing them. Rely on US“Unfortunately, for scientific assistants, there is not much we can do right now except rely on US frontier AI,” said Antoine Bosselut, a natural language processing expert based at EPFL. Chinese-built open models, which can run on local computers, lessening the risk of a shutdown, are touted as one alternative to US dependence. There’s also Mistral, once seen as Europe’s main AI hope, but the Paris-based company has seen its recent models fall behind in performance. But the Chinese alternatives are generally not as capable as US frontier AI models, said Bosselut, unless paired with fiddly, custom tools that allows them to act as independent agents. “Most scientists do not have the time,” he said. Don’t jump ship yetAlthough the kill switch moment is seen a warning shot for European scientists, it doesn’t mean labs should abandon US AI tools immediately. “I do not think the right response is simply to abandon US-based models and systems under the banner of sovereignty or independence,” said Mattias Björnmalm, secretary general of the Cesaer university association. “That would be an overreaction.”Related articlesEuropean sovereignty in AI requires ‘ugly trade-offs,’ say experts Robotic lab start-up makes case for €500M EU-funded materials testing facilityAI in science: is it useful? Instead, European researchers need to ask themselves whether they’re becoming dependent on technology that could be turned off in Washington, said Özcan. “They might not see this today, but they should really start asking this question,” she said. European researchers need a plan B, said Salathé. “I would recommend to every researcher, very proactively explore alternatives.”Own toolsIn addition, Björnmalm and others want European research institutions to develop their own scientific AI tools.Instead of signing AI deals with US digital giants, European universities should be using this money to create their own models, which may end up being superior for research, said Sabine Leonelli, an expert on the use of AI in science at the Technical University of Munich. “Universities in Europe seem to be happy to sign commercial contracts with US companies on the assumption [their AI tools] are better,” she said. But US frontier LLMs are typically opaque, and have been developed without independent scientific input, she said. “This is not good enough for science.”Last year, Swiss researchers released Apertus, an LLM designed to be completely transparent, from its development methods to its training data. The idea is that this makes scientific tools built on top of the model far more reproducible than with closed, commercial alternatives.“Europe needs more of this: serious capacity-building that gives researchers, universities and companies credible options,” said Björnmalm.
At ISC, JUPITER Shows What Exascale Science Looks Like
Europe’s first exascale supercomputer — running on NVIDIA Grace Hopper Superchips — is mapping the brain, modeling climate, advancing 6G AI and breaking records in quantum computing simulation.
NVIDIA Vera Rubin Delivers World-Class Supercomputers for Science
With 7 Exaflops of AI for Science and 5 Petaflops of Native FP64 Performance, Vera Rubin Packs TOP500 Supercomputing in a Single RackNews Summary: NVIDIA Vera Rubin accelerates the world's ...
Discovery Supercomputer’s first applications selected for Day-One Science
Scientific computing experts at the Department of Energy’s Oak Ridge National Laboratory have selected the first nine research projects that will run on Discovery, the nation’s next flagship supercomputer, slated for deployment in 2028. The projects will be developed through the Discovery Center for Accelerated Application Readiness program at ORNL’s Oak Ridge Leadership Computing Facility. The program is designed to ensure that a suite of leading-edge scientific applications is optimized and ready to deliver results as soon as Discovery becomes operational.
Discovery Supercomputer’s First Applications Selected for Day-One Science
Scientific computing experts at the Department of Energy’s Oak Ridge National Laboratory (ORNL) have selected the first nine research projects that will run on Discovery, the nation’s next flagship supercomputer, slated for deployment in 2028.The projects will be developed through the Discovery Center for Accelerated Application Readiness (CAAR) program at ORNL’s Oak Ridge Leadership Computing Facility (OLCF).
Supercomputer illuminates subatomic particle that helps hold matter together
A team of researchers has leveraged a supercomputer at the U.S. Department of Energy's (DOE) Argonne National Laboratory to reveal the internal structure of a pion in unprecedented detail. The findings ...
Fermilab storage infrastructure enables AI-driven scientific and research discovery for DOE’s Genesis Mission
The U.S. Department of Energy’s Genesis Mission is building a new era of AI-driven scientific discovery — and it requires far more than powerful supercomputers to succeed. To support this national effort, Fermilab’s Fermi Data Platform is providing secure, large-scale data infrastructure needed to make advanced AI research possible across the American Science Cloud.
ORNL scientist: How AI helps scientists predict Earth's future
Oak Ridge scientist Forrest Hoffman explains how AI and supercomputers help predict Earth's complex climate systems.
France rated second favourites for World Cup by Opta supercomputer
France have been identified as the second favourites to win the 2026 FIFA World Cup according to predictions generated by Opta’s supercomputer ahead of the tournament.With just over a week remaining...
New AI Inference Service Now Ready for Science at Argonne
AI can help accelerate scientific discovery, but setting up and running a foundation model is not a simple task. Thanks to the work of the Argonne Leadership Computing Facility, scientists affiliated with DOE National Labs and the Genesis Mission can now tap into a new AI inference service running on ALCF supercomputers. Dubbed the ALCF […]
Massive supercomputer simulations unlock cosmic magnetic mystery
Scientists used some of the most advanced plasma simulations ever created to uncover how the universe builds enormous magnetic fields out of turbulence. The discovery could reshape our understanding of stars, black holes, neutron star collisions, and dangerous solar eruptions.
Premier League predictions: How accurate were BBC Sport pundits?
Last summer, 33 BBC pundits - plus AI and Opta's 'supercomputer' were asked to pick which teams would finish in the Premier League's top four, and in which order. How did they get on?
Scientists were wrong about this “rule-breaking” particle
Scientists spent decades chasing signs of a mysterious new force hidden inside the muon, one of nature’s strangest particles. But after years of supercomputer calculations, researchers discovered the apparent anomaly was likely a calculation error — and the Standard Model still reigns supreme.
New MatterChat Model Helps AI to ‘See’ the Language of Science
From writing emails to generating computer code, much of the artificial intelligence prevalent in our daily lives has succeeded by mastering one domain: text. However, this leaves a major blind spot in the physical sciences, where models depend on the high-resolution, three-dimensional data of the physical world, like the intricate lattice of atoms in a crystal. Delivering on the promise of using AI for science requires teaching these data-driven text models to seamlessly “talk to” physics-based models. Now, a new AI framework from Lawrence Berkeley National Laboratory (Berkeley Lab), called MatterChat, solves this problem by creating a specialized “bridge.” It connects the conversational power of a Large Language Model (LLM) with a physics-based AI that models “interatomic potentials”: the complex physical forces between atoms. The resulting system already significantly outperforms general-purpose AI tools like GPT-4 at predicting material properties, and the team hopes it can accelerate scientific discovery by serving as a robust research partner that provides grounded insights and generates step-by-step instructions for synthesizing novel materials. A paper describing this work was recently published in Nature Machine Intelligence. “Traditional simulations can provide the physical rigor required for materials science, yet their computational cost remains prohibitive for high-throughput screening. Conversely, while LLMs excel at rapid knowledge synthesis, they inherently lack the ‘structural vision’ to interpret materials directly from their underlying atomic coordinates,” said Yingheng Tang, a postdoctoral researcher in Berkeley Lab’s Applied Math and Computational Research Division (AMCR) and lead author on the paper. “MatterChat was built to solve this dilemma, empowering LLMs with a structural ‘vision’ that allows researchers to leverage their full potential for solving complex, real-world materials challenges.” Empowering language models to solve complex challenges in materials science To build MatterChat, the Berkeley Lab team drew inspiration from technologies like Vision Question Answering (VQA) and Text-to-Image (T2I) generation. In these tasks, AI must translate high-level text concepts into visual images, or vice versa. Doing so requires developers to build tools that “bridge” two fundamentally different forms of data. The researchers adapted this concept to the physical sciences. With MatterChat, they created a “bridge model” that successfully connects an LLM’s general knowledge with the deep understanding of the atomic-scale world encoded in scientific interatomic potentials. Until now, researchers using LLMs to solve materials problems usually had to feed them raw data files as if they were just strings of text. It’s like asking an AI to understand a complex 3D engine based only on a parts list: the LLM can read the names, but it can’t “see” how the atoms fit together in space. MatterChat solves this by training a specialized AI bridge model, pre-trained on millions of crystal structures and an LLM, to align the LLM’s representation of the world with the interatomic potential’s representation of the world. The bridge model can translate physical insights into a format the LLM can actually understand. By giving the LLM these “scientific eyes”— a scientific “inductive bias” in the terminology of AI — the Berkeley Lab team has transformed it into a robust research tool capable of providing grounded scientific insights into complex materials challenges, such as predicting thermal stability or analyzing electronic band gaps. “We think of atoms as living in a physical space, but from a machine learning perspective, they are just vectors living in some very non-trivially structured manifold in a high-dimensional Euclidean space; and, of course, the same is true for the sentences and paragraphs that we use to express our ideas about those atoms,” said co-author Michael Mahoney, Berkeley Lab’s Scientific Data Division (SDD) AI Initiative Research Lead. “The bridge model basically gets those two structures to ‘talk with’ each other.” As a proof-of-concept of this general approach, the team trained their bridge model on a dataset curated by pairing nearly 143,000 stable atomic structures from the Materials Project with their corresponding physical properties. This training data was automatically assembled using the Materials Project’s API and deliberately enriched with properties fundamental to microelectronics design — like formation energy and bandgap — allowing MatterChat to learn the complex patterns connecting a material’s atomic blueprint to its functional performance. To validate their model, the researchers benchmarked MatterChat against a suite of other AI systems, from general-purpose LLMs to other specialized scientific AI methods. The results show that MatterChat consistently outperformed its competitors across a range of tasks. The model was more accurate in classifying material types and demonstrated superior precision in predicting numerical properties. For example, it excelled at predicting a material’s bandgap, a property critical for designing new electronics from high-capacity energy storage to next-generation computer chips. “Our design is significantly more efficient because we don’t have to build a massive AI model from the ground up,” said co-author Zhi (Jackie) Yao, a research scientist in Berkeley Lab’s AMCR. “Instead, we take two powerful, pre-trained models — a structural encoder for materials physics and an open-source LLM — and use them off-the-shelf. The only component we actually train is the lightweight ‘bridge model’ that translates between them. It’s the difference between building an entire car factory and simply designing a smart adapter that connects a world-class engine to a world-class navigation system. This approach is not only computationally efficient, but also makes the system modular, so we can easily upgrade components or adapt the bridge for other scientific domains in the future.” Crucially, this modular design highlights exactly how institutions like Berkeley Lab and the Department of Energy are carving out a highly valuable niche in the booming AI landscape. Rather than competing with Silicon Valley tech giants to build ever-larger language models from scratch, the lab is focusing on the specialized connective tissue that makes commercial AI useful for hardcore science. Because the bridge model approach underlying MatterChat is forward-compatible, it is perfectly positioned to leverage these parallel tracks of innovation. As Mahoney pointed out, “We expect that industry will continue to develop improved LLMs, and we expect domain scientists and facilities will continue to generate new data. An important part of scientific machine learning is not simply to solve problems on today’s data, but instead to develop general methods that will be forward-compatible with orders of magnitude more data, whether from scientific domains or from LLMs.” According to Yao, the MatterChat project, which was initially developed and enhanced with funding from a Berkeley Lab Laboratory Directed Research & Development (LDRD) Program, will now expand its capabilities. In a collaboration with Fermilab, MatterChat is already contributing to a U.S. Department of Energy Genesis Mission project — called Accelerating eXtreme Environment Specs-to-Silicon (AXESS) — that aims to speed up the development of next-generation, high-speed, radiation-hardened detectors for challenging particle physics experiments by using advanced 3D integrated circuits (chiplets) and AI-driven data analysis. In addition to the LDRD support, the team also credits supercomputing resources at the National Energy Research Scientific Computing Center (NERSC), located at Berkeley Lab, with MatterChat’s success. “We are incredibly grateful to NERSC; this research simply would not have happened without access to the Perlmutter supercomputer through their AI for Science program,” said Tang. Wenbin Xu, a NERSC postdoctoral fellow at the time, was also a major co-author of the work, as was Benjamin Erichson, a research scientist in Berkeley Lab’s SDD, highlighting the benefits of AMCR-SDD collaboration on AI for science. ### Lawrence Berkeley National Laboratory (Berkeley Lab) is committed to groundbreaking research focused on discovery science and solutions for abundant and reliable energy supplies. The lab’s expertise spans materials, chemistry, physics, biology, earth and environmental science, mathematics, and computing. Researchers from around the world rely on the lab’s world-class scientific facilities for their own pioneering research. Founded in 1931 on the belief that the biggest problems are best addressed by teams, Berkeley Lab and its scientists have been recognized with 17 Nobel Prizes. Berkeley Lab is a multiprogram national laboratory managed by the University of California for the U.S. Department of Energy’s Office of Science. DOE’s Office of Science is the single largest supporter of basic research in the physical sciences in the United States, and is working to address some of the most pressing challenges of our time. For more information, please visit energy.gov/science.
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