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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.
Fermilab teams up with NIU to launch quantum science program
Fermilab and Northern Illinois University are joining forces to offer a unique Master of Science in Physics program specializing in quantum science and technology. This innovative graduate program combines the expertise of a DOE national laboratory quantum center and a prominent state university to prepare the next generation of quantum experts.
When open science meets real-world cybersecurity
CISO discusses research-driven cybersecurity challenges at Fermilab, from availability-first systems to securing legacy infrastructure.
Scientists Just Tore Up a Major Particle Physics Theory
New results from the MicroBooNE experiment at Fermilab found no evidence of a hypothetical fourth flavor of neutrino
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.
Fermilab celebrates 2026 quantum science program graduates
This past weekend, a new group of high school students graduated from the Saturday Morning Quantum program. Hosted by Fermilab, the course allows students to engage directly with lab quantum scientists and engineers while exploring core topics in quantum science.
Scientists spent 10 years chasing a particle that wasn’t there
After a decade of painstaking measurements, scientists have delivered a major plot twist in particle physics: a long-hypothesized “mystery particle” likely doesn’t exist. Using the MicroBooNE experiment at Fermilab, researchers analyzed neutrinos from two powerful beams and found no evidence for a sterile neutrino, ruling it out with 95% certainty.
ICARUS experiment marks major milestone in first neutrino science results
The milestone by ICARUS paves the way to probe sterile‑neutrino oscillations for the full Short-Baseline Neutrino Program at Fermilab. The first results demonstrate the data’s excellent quality and suitability for physics analyses, as well as the maturity of the software tools for event selection, fitting and detector simulation.
2026 guest artist and composer transform Fermilab science into art
Fermi Forward Discovery Group’s annual guest artist and composer program fosters creative collaborations that help translate Fermilab’s complex scientific research into accessible, engaging experiences. In 2026, visual artist Eleftheria Lialios and composer Isaac Smith will bring their distinct perspectives to transform particle physics data into immersive visual and auditory works for the public.
Fermilab to lead two new Genesis Mission awards to accelerate development of microelectronics and quantum
Two new awards will expand Fermilab’s critical role in the Genesis Mission by developing custom microelectronics and advancing quantum information science.
Associate directors help shape future of Fermilab
Fermilab’s recent reorganization brings its accelerator, physics and technology programs under three unified directorates, each charged with advancing the laboratory’s most important scientific priorities. Together, these directorates form a coordinated leadership structure designed to deliver world class research and guide Fermilab’s future.
Fermilab marks 70 years of neutrino science and leads next-generation experiment
As Fermilab commemorates 70 years since the discovery of the neutrino at the Savannah River Site in the United States, the lab also celebrates its multiple generations of pioneering neutrino research. Today, Fermilab is building the next era of neutrino discovery through the development of the world-leading Deep Underground Neutrino Experiment.
Anna Grassellino appointed to DOE Office of Science Advisory Committee
Anna Grassellino of Fermilab will serve on SCAC and chair the quantum subcommittee, tasked with advancing national quantum goals.
Scientists travel to Fermilab to discuss future of high-energy physics
Fermilab recently hosted over 500 physicists for the American Physical Society’s biennial Division of Particles and Fields meeting. With presentations by Fermilab Director Norbert Holtkamp and CERN Director-General Mark Thomson, the meeting provided particle physicists the opportunity to come together and consider the future of high-energy physics research.