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Mars Rover 2020

More than 50 current employees and recent retirees from ORNL received Department of Energy Secretary’s Honor Awards from Secretary Jennifer Granholm in January as part of project teams spanning the national laboratory system. The annual awards recognized 21 teams and three individuals for service and contributions to DOE’s mission and to the benefit of the nation.

Three ORNL scientists have been elected fellows of the American Association for the Advancement of Science, or AAAS, the world’s largest general scientific society and publisher of the Science family of journals. Credit: ORNL, U.S. Dept. of Energy

Three ORNL scientists have been elected fellows of the American Association for the Advancement of Science, or AAAS, the world’s largest general scientific society and publisher of the Science family of journals.

ORNL Corporate Research Fellow and Geospatial Science and Human Security Division Director Budhu Bhaduri has been elected as a fellow of the American Association of Geographers. The honor recognizes Bhaduri for his “innovation, mentorship and wide-ranging leadership” in geographic sciences. Credit: ORNL, U.S. Dept. of Energy

ORNL’s Budhendra “Budhu” Bhaduri has been elected a fellow of the American Association of Geographers. The honor recognizes Bhaduri as “a world leader in innovation, development and application of research in human dynamics, geographic data science, remote sensing and scalable geocomputation.”

Biofuels, such as those derived from the switchgrass being harvested in this field in Vonore, Tennessee, are just one of the technology-based solutions that ORNL summit participants identified recently as key to decarbonizing the agriculture sector. Credit: Erin G. Webb, ORNL/U.S. Dept. of Energy.

Energy and sustainability experts from ORNL, industry, universities and the federal government recently identified key focus areas to meet the challenge of successfully decarbonizing the agriculture sector

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.