November 2020   |   Climate, Exascale Science

Climate on a new scale

DOE’s Energy Exascale Earth System Model will harness the next level of supercomputer to explore big climate questions.

Computer Science, Energy
October 2020

Banishing blackouts

An Argonne researcher upgrades supercomputer optimization algorithms to boost reliability and resilience in U.S. power systems.

Artificial Intelligence, Machine Learning
September 2020

AI gets real

From new energy materials to drug discovery and subatomic physics, artificial intelligence programs are creating scientific knowledge and applications across Department of Energy national labs.

Materials Science, Quantum computing
August 2020

Quantum quandary

Simulations on Oak Ridge’s Summit supercomputer take aim at new energy-transmission devices and quantum computers.

Science Highlights

August 2020

Computing nuclei properties at lightning speed

Physicists draw from Oak Ridge’s Summit supercomputer to train personal computers to calculate atomic nuclei properties in about an hour.

Nuclear physicists have developed a new method for quickly emulating the properties of atomic nuclei – quantum objects whose properties are complex and cannot be explained by classical physics. The new method helps scientists understand those quantum properties. The emulator allows a standard personal computer to approach these quantum problems in less than an hour, starting with a training stage that uses a small set of exact calculations from the Oak Ridge Leadership Computing Facility’s Summit supercomputer. The emulator then generates 1 million predictions for the ground-state energy and charge radius of nuclei of the isotope oxygen-16.

Researchers have long sought to uncover the properties of the interaction that binds protons and neutrons to atomic nuclei. But analyzing millions of exact samples of a complex nucleus would take the Summit supercomputer more than a year. This new method dramatically reduces the computational complexity of the many-nucleon problem and introduces new possibilities to systematically quantify uncertainties in first-principle atomic nuclei computations.

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