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New Hybrid Approach Bypasses Hardware Limits to Unlock Complex Quantum Simulations

A schematic diagram divided into two main sections illustrating a computational workflow, with Quantum measurements on the left and Classical postprocessing on the right. The left section displays a series of quantum circuit diagrams at increasing time steps, featuring green blocks labeled Hamiltonian simulation and blue blocks labeled Shallow shadows, which output histograms and color-coded data vectors. The right section shows the flow of classical data processing, starting at the top with multi-layered, colorful grids representing data matrices being used to compute a central system matrix. An arrow points down from this matrix to a diagram labeled Spectral decomposition, which shows data points plotted around the perimeter of a circle to represent eigenvalues. A final arrow points left to a line graph labeled Energy error, illustrating the error rates for both an excited state and a ground state steadily decreasing as the number of data points increases.

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.

AMCR’s Aditi Krishnapriyan Receives 2025 Early Career Award

Aditi Krishnapriyan, a woman with long dark hair, smiles while standing outside in front of green trees and a bright sky.

Mathematical Innovation Advances Complex Simulations for Science’s Toughest Problems

A two-part diagram illustrating a mathematical framework for simplifying complex models. The top diagram shows a V-shaped path of blue arrows enclosing a horizontal line that extends from a point labeled '0' towards an infinity symbol. Small circles along the line represent key numbers describing a system's dynamics. The blue path illustrates a calculation where orange and green segments at the far right can be ignored, allowing scientists to focus on the system's essential features for faster results. The bottom diagram shows a horizontal line with colored tick marks representing a random process visiting different states over time. Above the line, red brackets group larger sets of states into repeating loops, or cycles. Below the line, blue brackets highlight more specific cycles. This illustrates how analyzing these cycles can simplify and speed up complex simulations.

Berkeley Lab Researchers Evaluate Generative AI Models for Filling Scientific Imaging Gaps

A comparative infographic showcasing the results of different generative AI models in creating scientific images. The infographic is divided into three vertical sections, labeled 'Ceramics,' 'Plants,' and 'Rocks.' Each section contains a grid of images comparing a 'Raw' scientific source image to AI-generated versions created by models including DCGAN, StyleGAN, DALL-E2, and DALL-E3. The 'Ceramics' section shows microscopic views of packed particles, the 'Plants' section displays images of seedlings and root systems, and the 'Rocks' section features grayscale micro-scans of rock sediment, illustrating the varying success of different AI models in reproducing scientifically accurate images.

New AI-Powered Hybrid Simulation Reveals How Electrons Drive Chemical Reactions in Liquids

A scientific diagram showing a four-step simulation. Step (a) shows a system of water molecules with an excess electron. Step (b) shows a simplified representation of a single water molecule. Step (c) is a heat map of the system's electrical potential. Step (d) shows the final calculated probability cloud of the electron, derived from the potential in (c).

Unlocking the Power of Quantum Computing with Practical Benchmarking Tools

Close-up view of a quantum computer’s superconducting processor housed within a cylindrical, gold-colored cryogenic chamber, featuring multiple stacked plates connected by intricate wiring and metallic rods.

New AI Model Advances Long-Term, Multi-Scale Prediction of Complex Physical Systems

Four-panel visualizations for three different physical systems are arranged in three rows, labeled (a), (b), and (c). Each row corresponds to a specific system: (a) plasma turbulence (MHD), (b) fluid flow (Navier-Stokes), and (c) shallow-water dynamics. Within each row, the panels display (from left to right): the ground truth state, the StFT-F model prediction, the spatial distribution of prediction error (residual), and the predicted uncertainty. Colorbars beside each panel indicate the value ranges, with red and blue representing positive and negative values. The visualizations show that areas with higher errors generally coincide with regions of higher predicted uncertainty.

HPSF Receives HPCwire Readers’ Choice Award for Best Collaboration

Digital simulation generated with the WarpX code depicts blue and yellow shockwaves against a black background.

Unprecedented Perlmutter Simulation Details Quantum Chip

Berkeley Lab Hosts SIAM NCC25

A large group of people pose together for a group photo in front of a modern building labeled "SHYH WANG HALL 59." The group is diverse, including men and women of various ages, and some people are wearing conference badges. The photo was taken on a sunny day, and the building features large windows and gray metal paneling.

NERSC Supports Gordon Bell Prize Winner and Finalist

A group of individuals stands on a stage beneath a large "SC25" sign with a purple dynamic background; several people hold award certificates, and others stand in formal or business attire.
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