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2026 Alvarez Fellow Samson Wang Bridges Quantum Theory and Practice

Samson Wang smiling warmly at the camera while standing outdoors in front of a backdrop of dense green leaves. They have short dark hair with bangs and are wearing round wire-rimmed glasses and an olive green button-down shirt.

Samuel Williams Honored with E.O. Lawrence Award

Headshot of Berkeley Lab senior scientist Samuel Williams.

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.

Paul Hargrove Reflects on 27 Years at Berkeley Lab

A professional headshot of Paul Hargrove with long brown hair pulled back into a ponytail. He is wearing wire-rimmed glasses and a dark polo shirt against a mottled gray background.

Expert Insight: Keith Beattie and the Rise of the RSE

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."

Excited-States Framework Could Illuminate New Optical Materials

NERSC Director Sudip Dosanjh to Retire

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.
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