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QIS Project Shows Novel Method for Privacy-Preserving Quantum ML

April 20, 2023

Scientific results from the initial [email protected] projects are starting to emerge; in one recently published paper, a research group shared results of a quantum machine learning project that explores novel methods for preserving privacy within advanced quantum computing functions.
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Computational Modeling Streamlines Hunt for Battery Electrolytes

April 11, 2023

Using computing resources at NERSC at Berkeley Lab, researchers from the Joint Center for Energy Storage Research have identified new, more efficient ways to find improved electrolytes for batteries. By computationally modeling molecules and virtually observing their properties, researchers can identify the most promising ones and save experimental scientists from spending time and resources on those that won’t work. Read More »

New Math Methods and Perlmutter HPC Combine to Deliver Record-Breaking ML Algorithm

March 13, 2023

Using the Perlmutter supercomputer at the National Energy Research Scientific Computing Center (NERSC), researchers at Lawrence Berkeley National Laboratory (Berkeley Lab) have devised a new mathematical method for analyzing extremely large datasets – and, in the process, demonstrated proof of principle on a record-breaking dataset of more than five million points. Read More »

Shining a light on electrons’ role in energy transfer among 2D materials

March 6, 2023

Using the Cori supercomputer, a group of scientists examined heat and energy movements between certain 2D materials, spurring discoveries that could pave the way for a new generation of transistors Read More »

Berkeley Lab Works Toward a Connected Future for Science

February 27, 2023

Superfacility is a conceptual model of seamless connection between experimental facilities and high performance computing resources – an integrated and automated system for gathering, transporting, and analyzing scientific data in real time. Berkeley Lab is working to standardize, automate, and scale up the processes needed for superfacility onsite across the U.S. Department of Energy (DOE), and beyond. Read More »

The Most Advanced Bay Area Earthquake Simulations Will be Publicly Available

February 10, 2023

A collaboration involving scientists and computing resources from Berkeley Lab and the simulation software EQSIM is releasing the most accurate and detailed earthquake simulations to date, which will initially capture earthquake motions across the San Francisco Bay Area and later expand to other regions. Read More »

WarpX Code Shines at the Exascale Level

February 2, 2023

The WarpX project has spent the last six years creating a novel, highly parallel, and highly optimized single-source simulation code for modeling plasma-based particle colliders on cutting-edge exascale supercomputers, with broad importance for other accelerators and related problems. Read More »

Berkeley Lab Scientists Create Machine Learning Pipeline for Interpreting Large Tomography Datasets

January 25, 2023

A group of Berkeley Lab scientists has developed and tested several machine learning techniques organized in a learning pipeline to improve the interpretation of increasingly large cryo-ET datasets. Read More »

Perlmutter Results Show Progress in Quantum Information Science

January 23, 2023

The [email protected] initiative at NERSC aims to support research in the space of quantum information science (QIS) conducted on the Perlmutter supercomputer, including quantum simulation of materials and chemical systems, algorithms for compilation of quantum circuits, error mitigation for quantum computing, and development of hybrid quantum/classical algorithms. The first phase of the project will come to an end this month, but has begun to bear fruit in the form of early science results and collaborations across the field. Read More »

New Search Method Expands Horizon in Hunt for New Polymer Electrolytes

January 17, 2023

Using computing resources at NERSC, researchers at MIT, in partnership with the Toyota Research Institute, have pioneered a new method for using machine learning to screen for new materials, yielding the largest dataset of polymer electrolytes ever seen in the field and signaling progress for the search for new materials generally. Read More »