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Berkeley Lab researchers developed an AI algorithm to optimize ion beams at RHIC, enhancing collision precision and advancing insights into atomic structure and the Universe’s origins.
Berkeley Lab’s EScAIP is a machine-learning method that accelerates atomistic simulations, reducing memory usage by more than fivefold and boosting speed by over tenfold.
Honoring her groundbreaking contributions to Earth and environmental science, this accolade recognizes her commitment to advancing informatics and fostering collaboration within the scientific community.
FerroX, a new open-source, 3D simulation framework, could advance record-breaking energy efficiency in microelectronics.
A leading expert in quantum computing, Bert de Jong discusses why quantum computing matters, where it is today, and the potential of this emerging technology to change science.
Berkeley Lab researchers have developed EScAIP, a groundbreaking machine-learning method that accelerates atomistic simulations by improving model scalability. This approach reduces memory usage by more than fivefold and delivers results over ten times faster than current models.
At the Relativistic Heavy Ion Collider (RHIC) researchers smash gold atoms at near-light speeds to unlock the Universe’s secrets. A Berkeley Lab-led team developed an AI algorithm to optimize ion beam intensity, boosting collision precision and pushing the boundaries of particle physics for deeper insights into atomic structure and the Universe’s origins.
On November 7 and 8, 2024, a group of researchers from the University of Tsukuba (UT) visited Berkeley Lab to learn about new projects in the Computing Sciences Area (CSA).
Deb Agarwal’s enduring commitment to creating lasting scientific impact has earned her recognition as a 2024 AGU Fellow, a prestigious honor acknowledging her transformative contributions to Earth and environmental science. She hopes this recognition will help bring greater visibility to the remarkable work of informatics professionals within AGU’s ranks.
Last month, over 100 academics, researchers, industry experts, and national lab staff gathered at Berkeley Lab’s Artificial Intelligence Science Summit (BLASS) to explore AI’s potential to drive scientific discovery.
With the rise of chiplets, AMCR’s John Shalf identified an opportunity for Berkeley Lab to collaborate with hyperscalers—like Amazon, Google, Microsoft, and others—through the Open Compute Project to leverage industry trends for HPC.
SciData transforms data-driven discovery and understanding through the development and application of novel data science methods, technologies, and infrastructures with scientific partners.
The Applied Mathematics & Computational Research Division conducts research and development in mathematical modeling and simulation, algorithm design, data storage, management and analysis, computer system architecture, and high-performance software implementation.
The Center for Advanced Mathematics for Energy Research Applications (CAMERA) is an integrated, cross-disciplinary center aimed at inventing, developing, and delivering the fundamental new mathematics required to capitalize on experimental investigations at scientific facilities.
The Quantum Systems Accelerator is catalyzing national leadership in quantum information science to co-design the algorithms, quantum devices, and engineering solutions needed to deliver certified quantum advantage in Department of Energy scientific applications. Berkeley Lab leads the center. Sandia National Laboratories is the lead partner.
