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The transportation and industrial sectors together account for more than 50% of the country’s carbon footprint. Defossilization could help reduce new emissions from these and other difficult-to-electrify segments of the U.S. economy.

Scientists at Oak Ridge National Laboratory and six other Department of Energy national laboratories have developed a United States-based perspective for achieving net-zero carbon emissions. 

A multidirectorate group from ORNL attended AGU23 and came away inspired for the year ahead in geospatial, earth and climate science

ORNL scientists and researchers attended the annual American Geophysical Union meeting and came away inspired for the year ahead in geospatial, earth and climate science. 

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. 
 

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 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. 

Frontier’s exascale power enables the Simple Cloud-Resolving E3SM Atmosphere Model to run years’ worth of climate simulations at unprecedented speed and scale. Credit: Ben Hillman/Sandia National Laboratories, U.S. Dept. of Energy

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.

Sangkeun “Matt” Lee received the Best Poster Award at the Institute of Electrical and Electronics Engineers 24th International Conference on Information Reuse and Integration.

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.

ORNL researchers are establishing a digital thread of data, algorithms and workflows to produce a continuously updated model of earth systems.

Digital twins are exactly what they sound like: virtual models of physical reality that continuously update to reflect changes in the real world.

 

A small droplet of water is suspended in midair via an electrostatic levitator that lifts charged particles using an electric field that counteracts gravity. Credit: Iowa State University/ORNL, U.S. Dept. of Energy

How do you get water to float in midair? With a WAND2, of course. But it’s hardly magic. In fact, it’s a scientific device used by scientists to study matter.

ORNL’s Climate Change Science Institute and Georgia Tech co-hosted a Southeast Decarbonization Workshop in November 2023. Credit: ORNL, U.S. Dept. of Energy

ORNL's Climate Change Science Institute and the Georgia Institute of Technology hosted a Southeast Decarbonization Workshop in November that drew scientists and representatives from government, industry, non-profits and other organizations to