Researchers at the Statewide California Earthquake Center are unraveling the mysteries of earthquakes by using physics-based computational models running on high-performance computing systems at ORNL. The team’s findings will provide a better understanding of seismic hazards in the Golden State.
ORNL climate modeling expertise contributed to a project that assessed global emissions of ammonia from croplands now and in a warmer future, while also identifying solutions tuned to local growing conditions.
Scientists at ORNL are looking for a happy medium to enable the grid of the future, filling a gap between high and low voltages for power electronics technology that underpins the modern U.S. electric grid.
New computational framework speeds discovery of fungal metabolites, key to plant health and used in drug therapies and for other uses.
The 2023 top science achievements from HFIR and SNS feature a broad range of materials research published in high impact journals such as Nature and Advanced Materials.
On Nov. 1, about 250 employees at Oak Ridge National Laboratory gathered in person and online for Quantum on the Quad, an event designed to collect input for a quantum roadmap currently in development. This document will guide the laboratory's efforts in quantum science and technology, including strategies for expanding its expertise to all facets of the field.
Scientists from more than a dozen institutions have completed a first-of-its-kind high-resolution assessment of carbon dioxide removal potential in the United States, charting a path to achieve a net-zero greenhouse gas economy by 2050.
A 19-member team of scientists from across the national laboratory complex won the Association for Computing Machinery’s 2023 Gordon Bell Special Prize for Climate Modeling for developing a model that uses the world’s first exascale supercomputer to simulate decades’ worth of cloud formations.
A team of eight scientists won the Association for Computing Machinery’s 2023 Gordon Bell Prize for their study that used the world’s first exascale supercomputer to run one of the largest simulations of an alloy ever and achieve near-quantum accuracy.
Lee's paper at the August conference in Bellevue, Washington, combined weather and power outage data for three states – Texas, Michigan and Hawaii – and used a machine learning model to predict how extreme weather such as thunderstorms, floods and tornadoes would affect local power grids and to estimate the risk for outages. The paper relied on data from the National Weather Service and the U.S. Department of Energy’s Environment for Analysis of Geo-Located Energy Information, or EAGLE-I, database.