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Hydrologist Jesus Gomez-Velez brings his expertise in river systems and mathematics to ORNL’s modeling and simulation research to better understand flow and transport processes in the nation’s watersheds. Credit: Genevieve Martin/ORNL, U.S. Dept. of Energy

Hydrologist Jesús “Chucho” Gomez-Velez is in the right place at the right time with the right tools and colleagues to explain how the smallest processes within river corridors can have a tremendous impact on large-scale ecosystems.

Oak Ridge National Laboratory materials scientist Zhili Feng, left, looks on as senior technician Doug Kyle operates a welding robot inside a robotic welding cell. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy

The U.S. Departments of Energy and Defense teamed up to create a series of weld filler materials that could dramatically improve high-strength steel repair in vehicles, bridges and pipelines.

Samarthya Bhagia examines a sample of a thermoplastic composite material additively manufactured using poplar wood and polylactic acid. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy

Chemical and environmental engineer Samarthya Bhagia is focused on achieving carbon neutrality and a circular economy by designing new plant-based materials for a range of applications from energy storage devices and sensors to environmentally friendly bioplastics.

ORNL scientists created a geodemographic cluster for the Atlanta metro area that identifies risk factors related to climate impacts. Credit: ORNL/U.S. Dept. of Energy

A new capability to identify urban neighborhoods, down to the block and building level, that are most vulnerable to climate change could help ensure that mitigation and resilience programs reach the people who need them the most.

Oak Ridge National Laboratory researchers used an invertible neural network, a type of artificial intelligence that mimics the human brain, to select the most suitable materials for desired properties, such as flexibility or heat resistance, with high chemical accuracy. The study could lead to more customizable materials design for industry.

A study led by researchers at ORNL could help make materials design as customizable as point-and-click.

Earth Day

Tackling the climate crisis and achieving an equitable clean energy future are among the biggest challenges of our time. 

ORNL scientists created a new microbial trait mapping process that improves on classical protoplast fusion techniques to identify the genes that trigger desirable genetic traits like improved biomass processing. Credit: Nathan Armistead/ORNL, U.S. Dept. of Energy. Reprinted with the permission of Oxford University Press, publisher of Nucleic Acids Research

ORNL scientists had a problem mapping the genomes of bacteria to better understand the origins of their physical traits and improve their function for bioenergy production.

ORNL biogeochemist Teri O’Meara is focused on improving how coastal systems are represented in global climate models, enabling better predictions about the future of these critical ecosystems. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy

Surrounded by the mountains of landlocked Tennessee, Oak Ridge National Laboratory’s Teri O’Meara is focused on understanding the future of the vitally important ecosystems lining the nation’s coasts.

ORNL’s Marie Kurz examines the many factors affecting the health of streams and watersheds. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy

Spanning no less than three disciplines, Marie Kurz’s title — hydrogeochemist — already gives you a sense of the collaborative, interdisciplinary nature of her research at ORNL.

This protein drives key processes for sulfide use in many microorganisms that produce methane, including Thermosipho melanesiensis. Researchers used supercomputing and deep learning tools to predict its structure, which has eluded experimental methods such as crystallography.  Credit: Ada Sedova/ORNL, U.S. Dept. of Energy

A team of scientists led by the Department of Energy’s Oak Ridge National Laboratory and the Georgia Institute of Technology is using supercomputing and revolutionary deep learning tools to predict the structures and roles of thousands of proteins with unknown functions.