Skip to main content

News

Marcus Noack Receives 2026 DOE Early Career Award

A headshot of Marcus Noack smiling, wearing a dark green knit sweater with wooden toggle buttons, standing against a blurred architectural background.

Berkeley Lab AI Learns Classic Physics to Scale Quantum Simulations

A multi-part technical diagram showing the architecture and function of the Sigma-Attention AI model. The left side shows a vertical flowchart titled "Encoder-only Transformer" with interconnected boxes labeled with AI processes like Multi-Head Attention and Feed Forward, leading to a final output labeled with a Sigma symbol. The top middle shows a chart titled "Dataset regime" that plots interaction strength against system size, shading three areas labeled MBPT for weak coupling, ED for intermediate coupling, and SCE for strong coupling. The top right features a line graph titled "Green's function prediction" displaying multiple U-shaped curves. Along the bottom, text reading "Generalizable self-energy learning across regime" sits next to a small, simple dot-and-line grid labeled "Small system." An arrow containing the AI model's Sigma symbol points from this small grid to a significantly larger grid labeled "Large system."

Zenesis: A ‘ChatGPT’ for Scientific Image Analysis

A split graphic illustrating the Zenesis AI tool in action. The left panel shows a raw grayscale scientific image of a textured material with a prominent, jagged dark crack running through it. The middle panel displays a chat interface where a user message asks, "what is the length of the largest crack?" An AI chatbot responds, stating it found the requested structure, highlighted it in green, and provides an estimated length of 904.15 pixels. The right panel displays the original scientific image, but the main crack is now precisely outlined in bright green, matching the AI's response.

A Berkeley Lab Mentorship Comes Full Circle

Three people stand smiling for a photo outside the entrance of a modern, grey-paneled building labeled "Shyh Wang Hall". From left to right: a man wearing sunglasses and a pink polo shirt, a woman wearing sunglasses and a bright yellow patterned top, and a young man wearing a black denim jacket over a black shirt with blue jeans. They are standing on a wide concrete walkway on a bright, sunny day.

DL4SCI Brings Researchers Into the Data Lifecycle

Two pairs of researchers discuss their work at a poster session

Novel Family of Conditional Generative Models Uses AI-Generated Earthquake Simulations to Improve Disaster Preparedness

A three-part diagram illustrating the workflow of the Conditional Generative Modeling for Ground Motion (CGM-GM) AI model. The left section shows a map of the San Francisco Bay Area with a red star marking an earthquake source and blue triangles representing seismic stations. The middle section features a three-dimensional, hourglass-shaped wireframe representing the CGM-GM model, compressing blue input data on the left into a narrow bottleneck and expanding it into red output data on the right. Below the model, three text boxes with icons indicate the system is trained on regional data, captures wave propagation, and accounts for subsurface heterogeneity. The right section displays the model's outputs: a heat map showing generated shaking intensity radiating from the earthquake source, with a color scale ranging from blue for low intensity to red for high intensity, and a line graph below it showing the detailed waveform of the generated ground motion.

Pele Combustion Simulation Suite Named R&D 100 Award Winner

A high-resolution 3D computer simulation of a turbulent flame expanding from left to right against a light grey background. The simulation shows a textured, silver-grey plume of fuel igniting into a bright core of blue, red, orange, and yellow colors representing intense heat and complex chemical reactions. In the top left corner is a black and gold badge that reads R&D 100 WINNER.

Software Highlight: AstraAI Masters the Complex Rules of Scientific HPC Software

A flowchart outlining the system architecture for AstraAI. The process begins on the left with a natural language user prompt, which is passed to an LLM for user intent extraction utilizing run-time tools like Hugging Face, Llama.cpp, or Ollama. The workflow then splits into two parallel processes: Retrieval Augmented Generation to extract relevant code chunks, and Abstract Syntax Tree code structure extraction utilizing Clang tools like intercept_build and clang-query. The outputs from these two processes are merged into a final prompt that includes the original user prompt, the RAG chunks, and the structure information. This final prompt is sent to an LLM for code generation, which ultimately leads to AST-safe code editing featuring GitHub conflict-style markers.

Meet EcoBOT: The Autonomous Lab Standardizing Plant-Microbe Research

A robotic arm in a high-tech laboratory holds up a small, clear plastic container that houses a single green plant seedling. Below the raised container is an out-of-focus grid of many similar clear plastic boxes. The scene is illuminated by bright, cool blue and green lights that reflect off the plastic and metallic machinery, giving it an automated, futuristic appearance.

Scientists Develop Predictive Roadmap to Boost Performance in Next-Gen Spintronics

Scanning electron microscope (SEM) image of a chiral 2D metal halide perovskite material synthesized with acetonitrile. Chiral perovskite films made with acetonitrile respond strongly to circularly polarized light.

New MatterChat Model Helps AI to ‘See’ the Language of Science

A conceptual diagram for "MatterChat: A Structure-Aware Multimodal LLM for Materials." The graphic shows a visual data flow starting from a 3D atomic structure on the left, passing through a bottleneck labeled "Bridge Model," and feeding into a digital interface labeled "LLM." The LLM interface lists various materials science terms like structure, property, synthesis, and stability, with a glowing AI brain floating on the far right.

OmniLearned Foundation Model Shows Promise Across Disciplines

Technical image of a hadronic jet
Computing Sciences logo

Connect

Our Organization