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Three graphs and one map with grey, long shapes showing the single block group.

Joe Tuccillo, a human geography research scientist, leads the UrbanPop project that uses census data to create synthetic populations. Using a Python software suite called Likeness on ORNL’s high-performance computers, Tuccillo’s team generates a population with individual ‘agents’ designed to represent people that interact with other agents, facilities and services in a simulated neighborhood. 

Ariel view of the Salt Waste Processing Facility, which is big, white and square.

A team of federal contractor and national laboratory engineers and scientists from the U.S. Department of Energy Office of Environmental Management has been nationally distinguished as “Heroes of Chemistry” for making the world better through their effort, ingenuity, creativity and perseverance.

Researcher Brittany Rodriguez works with an ORNL-developed Additive Manufacturing/Compression Molding system that 3D prints large-scale, high-volume parts made from lightweight composites. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy

Brittany Rodriguez never imagined she would pursue a science career at a Department of Energy national laboratory. However, after some encouraging words from her mother, input from key mentors at the University of Texas Rio Grande Valley, or UTRGV, and a lot of hard work, Rodriguez landed at DOE’s Manufacturing Demonstration Facility, or MDF, at Oak Ridge National Laboratory.

Oak Ridge National Laboratory building and sign for the Computing and Computational Sciences Directorate.

The contract will be awarded to develop the newest high-performance computing system at the Oak Ridge Leadership Computing Facility.

The photo is of a 3D-printed part -- a big grey block with a grey fan like structure coming out from the top. To the right shows a digital copy in an AI model.

The Department of Energy’s Oak Ridge National Laboratory has publicly released a new set of additive manufacturing data that industry and researchers can use to evaluate and improve the quality of 3D-printed components. The breadth of the datasets can significantly boost efforts to verify the quality of additively manufactured parts using only information gathered during printing, without requiring expensive and time-consuming post-production analysis.

Group of over 20 participants, both girls and boys, line up in a group with four rows of 13 in the quad - outside area of ORNL.

ORNL hosted the Mid-South Regional Chapter of the American Society for Photogrammetry and Remote Sensing, or ASPRS. Participants spanning government, academia and industry engaged in talks, poster sessions, events and workshops to further scientific discovery in a field devoted to using pictures to understand changes to the earth’s inhabitants and landscape. 

This photo is of three men sitting around a laptop computer that happens to be working on cybersecurity testing equipment.

A newly established internship between ORNL and Maryville College is bringing cybersecurity careers to a local liberal arts college. The internship was established by a Maryville College alumni who recently joined ORNL. 

Man in blue suit and blue and white button down with brown air and brown facial hair smiles for a photo with a green and teal background. Plus a quote

As a data scientist, Daniel Adams uses storytelling to parse through a large amount of information to determine which elements are most important, paring down the data to result in the most efficient and accurate data set possible.

Five girls stand outside of a red brick building with iron gate out front, Stephen King's house.

Participants in the SM2ART Research Experience for Undergraduates program got the chance to see what life is like in a research setting. REU participant Brianna Greer studied banana fibers as a reinforcing material in making lightweight parts for cars and bicycles.

Digital image of molecules would look like. There are 10 clusters of these shapes in grey, red and blue with a teal blue background

Oak Ridge National Laboratory scientists have developed a method leveraging artificial intelligence to accelerate the identification of environmentally friendly solvents for industrial carbon capture, biomass processing, rechargeable batteries and other applications.