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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.
Dominion stock price pops on NextEra merger to create utility giant
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BTS takes over Las Vegas—in more ways than one
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Scientists Discover Bizarre 100-Million-Year-Old Insect With Giant Claws
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A’s Drop Saturday Contest To Giants 6-4
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Stunning 150-million-year-old stegosaur skull rewrites dinosaur evolution
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Giants hire former Texans, Cardinals executive for front-office role
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The ‘Mech My Day’ showcase offers the most absurd match up in motorsport history
Scientists Decode Giant Venus Cloud, Say "Hydraulic Jump" Is Behind It
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🚨 Bundesliga: relegated sides set, one giant misses Champions League!
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UnitedHealth’s AI push now includes employee tracking
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Microsoft Stock (NASDAQ:MSFT) Jumps as Microsoft Plans to Roll Back Faulty Drivers
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AI camera blunder turns new Xperia 1 VIII into a meme: Sony roasted in viral social media trend
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Scientists Find Fossils Of Southeast Asia's Giant Dinosaur In Thailand
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Scientists discover giant “last titan” dinosaur, Southeast Asia’s largest ever
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Exclusive: At Samsung, the global AI boom spurred a looming strike and deep divisions
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Powerful New Cybersecurity Tools Give Microsoft Stock (NASDAQ:MSFT) a Boost
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Yankees urged to trade for Giants' 5.18 ERA veteran after Max Fried's elbow injury
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New York Giants will not play on Thanksgiving in 2026
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Giant “stealth” magma surge triggered thousands of earthquakes beneath Atlantic island
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Giant squid discovery uncovers a hidden deep-sea world off Australia
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Shirley Jones Named Entertainment Commissioning Head At ‘SNL UK’s Sky In Restructure; Barbara Lee To Exit
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Mastercard and U.S. Bank’s New Amazon Business Credit Cards
Dominant retail giant Amazon has launched a new Prime Businfess Card issued by U.S. Bank and Mastercard that offer shoppers rewards and flexible finance
Trump And Kennedy Seek To Relax Safeguards For AI Healthcare Tools
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A new species offers a clue to the boom of giant dinosaurs
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Where to watch San Francisco Giants vs. Los Angeles Dodgers: Live stream, TV channel, odds for Wednesday, May 13
The Dodgers take on the rival Giants.
Netflix co-CEO clarifies streaming giant’s live sports strategy amid NFL lineup expansion, federal scrutiny
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Netflix co-CEO clarifies streaming giant’s live sports strategy amid NFL lineup expansion, federal scrutiny
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Scientists finally solve the 100-year mystery behind tough tires
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Indian capital opposes Pernod's liquor plea, firm says business 'hopelessly fettered'
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New York Giants: Answering Biggest Offseason Questions
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Bayer Profit Up On Seed Business But Glyphosate Sales Struggle
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2026 NFL Odds: Giants Open as Underdogs vs. Cowboys in John Harbaugh's Debut
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Horrific video shows factory worker being pulled to his death into giant industrial shredder
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Michigan Science Center opens giant cardboard maze inspired by Detroit
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Saudi oil giant Aramco sees Q1 profits rise 25% by shifting exports to its East-West Pipeline
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Dua Lipa Sues Samsung for $15 Million After Company Refuses to Remove Her Photo From TV Boxes
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Game #40: Pittsburgh Pirates vs. San Francisco Giants
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Antarctica is melting from below and scientists say it’s worse than expected
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Giants' John Harbaugh on potential reunion with Odell Beckham Jr.: 'It's got to be right for both parties'
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Guardians acquire Patrick Bailey, two-time Gold Glove-winning catcher, in trade with Giants
Bailey has struggled with the bat, but is a well-rated defensive catcher
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Sleeping giant has awakened for Southern Section baseball playoffs
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Amazon Prime Video Leveraging Sports for Advertising Domination
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Fantasy Baseball Waiver Wire: Top hitting prospects from Yankees, Giants highlight weekend pickups
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Sony forecasts lower gaming business sales amid memory price surge
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Northern Colorado eyed for a giant, $350M entertainment venue
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The Universe’s biggest black holes may be forged in violent mergers
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Nature’s giant vacuum cleaner: Scientists reveal how the Sun is secretly cleaning Earth’s space junk
Science News: As Earth’s orbit becomes increasingly crowded with decommissioned satellites and fragments from the 1960s, however, researchers have identified a natu.
eXp enters the franchise business with NextHome acquisition
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Evidence of giant squid discovered — bodacious beast is bigger than a school bus with eyes the size of a large pizza
They're making major waves.
Giant slide at new Griffin Museum of Science and Industry exhibit set to be a scene stealer
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Disney CEO unveils entertainment giant's new 3-pillar growth plan
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Kraft Heinz sees early payoff from $600 million brand bet
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Scientists created one of the largest simulations of our universe ever — about the size of 500,000 HD movies
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CVS blows past estimates, hikes outlook as insurance business outperforms
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240-million-year-old giant “sand creeper” found hidden in retaining wall
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Bengals reunion chance over with DJ Reader headed to Giants
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Apple Reaches $250 Million Settlement Over Claims It Misled People on A.I.
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AMC Entertainment Q1 Revenue Jumps 20% Amid Box Office Recovery
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Apple reaches $250mn settlement over delayed ‘AI Siri’
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Former Lions nose tackle signing with New York Giants
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Wall Street opens up its partnership playbook for AI
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