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When an electron beam drills holes in heated graphene, single-atom vacancies, shown in purple, diffuse until they join with other vacancies to form stationary structures and chains, shown in blue. Credit: Ondrej Dyck/ORNL, U.S. Dept. of Energy

Oak Ridge National Laboratory researchers serendipitously discovered when they automated the beam of an electron microscope to precisely drill holes in the atomically thin lattice of graphene, the drilled holes closed up.

This diagram demonstrates how a concentrating solar thermal plant could use molten salts to store solar energy that could later be used to generate electricity. Credit: Jaimee Janiga/ORNL, U.S. Dept. of Energy

Oak Ridge National Laboratory scientists recently demonstrated a low-temperature, safe route to purifying molten chloride salts that minimizes their ability to corrode metals. This method could make the salts useful for storing energy generated from the sun’s heat.

Researchers used quantum Monte Carlo calculations to accurately render the structure and electronic properties of germanium selenide, a semiconducting nanomaterial. Credit: Paul Kent/ORNL, U.S. Dept. of Energy

A multi-lab research team led by ORNL's Paul Kent is developing a computer application called QMCPACK to enable precise and reliable predictions of the fundamental properties of materials critical in energy research.

Yun-Yi Pai works with a closed-cycle dilution refrigerator designed for cryomagnetooptical microscopy at ORNL. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy

Five National Quantum Information Science Research Centers are leveraging the behavior of nature at the smallest scales to develop technologies for science’s most complex problems.

Travis Humble. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy

Travis Humble has been named director of the Quantum Science Center headquartered at ORNL. The QSC is a multi-institutional partnership that spans industry, academia and government institutions and is tasked with uncovering the full potential of quantum materials, sensors and algorithms.

ORNL, VA and Harvard researchers developed a sparse matrix full of anonymized information on what is thought to be the largest cohort of healthcare data used for this type of research in the U.S. The matrix can be probed with different methods, such as KESER, to gain new insights into human health. Credit: Nathan Armistead/ORNL, U.S. Dept. of Energy

A team of researchers has developed a novel, machine learning–based  technique to explore and identify relationships among medical concepts using electronic health record data across multiple healthcare providers.

QLAN submit - A team from the U.S. Department of Energy’s Oak Ridge National Laboratory, Stanford University and Purdue University developed and demonstrated a novel, fully functional quantum local area network, or QLAN, to enable real-time adjustments to information shared with geographically isolated systems at ORNL using entangled photons passing through optical fiber. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy

A rapidly emerging consensus in the scientific community predicts the future will be defined by humanity’s ability to exploit the laws of quantum mechanics.

The Energy Exascale Earth System Model project reliably simulates aspects of earth system variability and projects decadal changes that will critically impact the U.S. energy sector in the future. A new version of the model delivers twice the performance of its predecessor. Credit: E3SM, Dept. of Energy

A new version of the Energy Exascale Earth System Model, or E3SM, is two times faster than an earlier version released in 2018.

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.