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New Material for Lower Cost Nuclear Fuel Recycling

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Computer-inspired materials discovery has led to the discovery of a new material that might help in nuclear fuel recycling and waste reduction by capturing certain gases released during reprocessing. By working at ambient temperature, the new material has the potential to save energy, make reprocessing cleaner and less expensive. The reclaimed materials can also be reused commercially. » Read More

Symmetry Breaking with Euclidean Neural Networks

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Curie's principle states that "when effects show certain asymmetry, this asymmetry must be found in the causes that gave rise to them". In this paper, we demonstrate that symmetry equivariant neural networks uphold Curie's principle and can be used to articulate many symmetry-relevant scientific questions into simple optimization problems.

Electronic Properties of Materials at 100 PFLOP/s

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Mauro Del Ben (CCMC) and Charlene Yang (NERSC) lead the optimization of the BerkeleyGW software package on leadership class HPC systems. By exploiting GPU acceleration they demonstrate for the first time the possibility of performing high-fidelity excited state calculations of complex materials at unprecedented scales within minutes on current HPC systems, paving the way for future efficient HPC software development in materials, physical, chemical, and engineering sciences. » Read More

Mekena Metcalf

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As quantum computers evolve from theory to functioning machines and begin solving problems beyond the capabilities of even the fastest supercomputers, finding a way to link these powerful new systems to communicate with other quantum machines becomes the next challenge.

Recently. Mekena Metcalf (CCMC postdoc), Anastasiia Butko (CAG), and Mariam Kiran (ESNet) of the Berkeley Lab Computing Sciences Area decided to pool their expertise after attending a workshop in San Francisco aimed at helping women project leadership confidence. The Berkeley Lab team is developing an approach to enable more efficient quantum transducers using deep reinforcement learning. » Read More

Materials Simulations on Quantum Computers 101

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The emergence of quantum computers provides a promising path forward for testing and analyzing the remarkable, and often counter-intuitive, behavior of quantum materials. In our recently released topical review, we aim to provide an accessible introduction to scientists interested in beginning to perform their own simulations of quantum materials on quantum computers. A pre-print may be found at:

Ultracompact Hamiltonian Eigenstates

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We have developed and analyzed an optimal version of a highly efficient quantum algorithm, variational quantum phase estimation (VQPE), for ground and excited state calculations of general many-body systems. VQPE allows for extraction of eigenvalues on near-term quantum hardware, important for solving problems in chemistry, materials science, and physics.

NWChem’s Planewave “Purrs” on Intel’s KNL Nodes

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A team of researchers at the Berkeley Lab, PNNL and Intel are working hard to make sure that computational chemists are prepared to compute efficiently on next-generation exascale machines. Recently, they achieved a milestone, successfully adding thread-level parallelism on top of MPI-level parallelism in the planewave density functional theory method within the popular software suite NWChem. » Read More

Researchers Catch Extreme Waves with High-Resolution Modeling

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Using decades of global climate data generated at a spatial resolution of about 25 kilometers squared, researchers were able to capture the formation of tropical cyclones, also referred to as hurricanes and typhoons, and the extreme waves that they generate. Those same models, when run at resolutions of about 100 kilometers, missed the tropical cyclones and the big waves up to 30 meters high. » Read More

Quantum Chemistry with Quantum Computers

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Berkeley Lab is preparing for quantum computing future and Luis Alvarez Fellow in Computing Science Jarrod McClean is hard at work to find ways to exploit this new computing paradigm to simulate and predict the chemistry and properties of advanced compounds before scientists go into the lab to make them. His work of yoking quantum processors with classical HPC systems into a hybrid computer was highlighted in ASCR Discovery. » Read More

Materials discoveries: don't dump the past

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Materials discoveries are typically driven by their potential for industrial and commercial applications. In a new Perspective article, Nils Zimmermann and Maciej Haranczyk unravel trends in zeolite discoveries. They find that current efforts in the field look similar to past trends that were little effective in establishing new mature technologies. » Read More

High Resolution Climate Simulations


Not long ago, it would have taken several years to run a high-resolution simulation on a global climate model. But using supercomputing resources at NERSC, climate scientist Michael Wehner of Berkeley Lab's Computational Research Division was able to complete a run in just three months » Read More

Major Speedups Through Intel Parallel Computing Center

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Quicker time to discovery. That’s what scientists focused on quantum chemistry are looking for. To achieve this, changes must be made in the HPC software used in quantum chemistry research to take advantage of advanced HPC systems to meet the research needs of scientists both today and in the future. LBL's Intel Parallel Computing Center advances are highlighted in Scientific Computing. » Read More

Assessing the Impact of Human-Induced Climate Change

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The past century has seen a 0.8°C (1.4°F) increase in average global temperature. What remains unclear is precisely what fraction of the observed changes in these climate-sensitive systems can confidently be attributed to human-related influences, rather than mere natural regional fluctuations in climate. In a recent Nature Climate Change paper a new method was applied to determine whether specific climate impacts can be traced to human-caused emissions. » Read More

Simulations Confirm Observations on 2015 India/Pakistan Heat Waves

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Three researchers from Berkeley Lab are co-authors on the paper, "The Deadly Combination of Heat and Humidity in India and Pakistan in Summer 2015," which examined observational and simulated temperature and heat indexes and concluded that the two separate heat waves were exacerbated by anthropogenic climate change. » Read More

The Computational Chemistry, Materials & Climate Group is focused on enabling scientific discovery through the development of advanced software applications, tools, and libraries in key research areas in chemistry, materials science and climate research, as well as the development of scientific computing applications and capabilities for the integration and analysis of complex data from simulation and experiment. Members of the group have expertise in domain science areas, applied mathematics, and computer science. The group develops

  • Scientific applications in areas such as atmospheric modeling and materials & chemical sciences

  • Methodologies and strategies for computational science, designing and implementing highly efficient computational kernels

Group Leader: Bert de Jong

Administrative Assistant: Rachel Lance