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Applied Computing for Scientific Discovery
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Symmetry Breaking with Euclidean Neural Networks

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 n…

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Electronic Properties of Materials at 100 PFLOP/s

Mauro Del Ben (ACSD) 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 t…

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AI for More Efficient Flying Qbits

A cross-Divisional collaboration is working on an AI approach to developing more efficient quantum transducers.

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Materials Simulations on Quantum Computers 101

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 top…

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Ultracompact Hamiltonian Eigenstates

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 s…

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Researchers Catch Extreme Waves with High-Resolution Modeling

Using decades of high-resolution global climate data, researchers were able to capture the formation of tropical cyclones and the extreme waves that they generate.

The Applied Computing for Scientific Discovery Group is focused on enabling scientific discovery through the development of advanced software applications, tools, and libraries in key scientific research areas, 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, quantum computing, machine learning, and computer science.

The group develops

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

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

  • Approaches to utilize exascale computers, quantum computing, and machine learning for scientific discovery.

Group Leader: Bert de Jong

Administrative Assistant: Rachel Lance