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ORNL’s Tomás Rush examines a culture as part of his research into the plant-fungus relationship that can help or hinder ecosystem health. Credit: Genevieve Martin/ORNL, U.S. Dept. of Energy

New computational framework speeds discovery of fungal metabolites, key to plant health and used in drug therapies and for other uses. 
 

ORNL’s Nagi Rao discusses the lab’s deployed “dark fiber” testbed for quantum networking at SC23 in Denver, Colorado. Credit: Mariam Kiran/ORNL, U.S. Dept. of Energy

ORNL’s successes in QIS and its forward-looking strategy were recently recognized in the form of three funding awards that will help ensure the laboratory remains a leader in advancing quantum computers and networks.

Caption: Jaswinder Sharma makes battery coin cells with a lightweight current collector made of thin layers of aligned carbon fibers in a polymer with carbon nanotubes. Credit: Genevieve Martin/ORNL, U.S. Dept. of Energy

Electric vehicles can drive longer distances if their lithium-ion batteries deliver more energy in a lighter package. A prime weight-loss candidate is the current collector, a component that often adds 10% to the weight of a battery cell without contributing energy.

2023 Top Science Achievements at SNS & HFIR

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.

The illustration depicts ocean surface currents simulated by MPAS-Ocean. Credit: Los Alamos National Laboratory, E3SM, U.S. Dept. of Energy

A team from DOE’s Oak Ridge, Los Alamos and Sandia National Laboratories has developed a new solver algorithm that reduces the total run time of the Model for Prediction Across Scales-Ocean, or MPAS-Ocean, E3SM’s ocean circulation model, by 45%. 

Alex May, pictured above, is the first and only full-time data curator at the Department of Energy’s Oak Ridge Leadership Computing Facility. Credit: Carlos Jones and Wikimedia Commons, background/ORNL, U.S. Dept. of Energy
Alex May is the first and only full-time data curator at the Department of Energy’s Oak Ridge Leadership Computing Facility, evaluating datasets developed by computational scientists before they are made public through the OLCF’s Constellation portal for open data exchange.
The AI agent, incorporating a language model-based molecular generator and a graph neural network-based molecular property predictor, processes a set of user-provided molecules (green) and produces/suggests new molecules (red) with desired chemical/physical properties (i.e. excitation energy). Image credit: Pilsun You, Jason Smith/ORNL, U.S. DOE

A team of computational scientists at ORNL has generated and released datasets of unprecedented scale that provide the ultraviolet visible spectral properties of over 10 million organic molecules. 

QSC Director Travis Humble, who gave a lunchtime talk on transitioning good ideas to device development, learns about one of the many quantum research efforts featured at the poster session. Credit: Alonda Hines/ORNL, U.S. Dept. of Energy

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.

ORNL researchers contributed biomass resources analysis to a new report that says carbon dioxide removal targets can be reached by 2050 using existing technology. Source: Jason Richards/ORNL, U.S. Dept. of Energy

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 Univ. of Michigan-led team used Frontier, the world’s first exascale supercomputer, to simulate a system of nearly 75,000 magnesium atoms at near-quantum accuracy. Credit: SC23

 

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.