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A new tool that simulates the energy profile of every building in America will give homeowners, utilities and companies a quick way to determine energy use and cost-effective retrofits that can reduce energy and carbon emissions.

A new tool that simulates the energy profile of every building in America will give homeowners, utilities and companies a quick way to determine energy use and cost-effective retrofits that can reduce energy and carbon emissions.

ORNL’s Jason DeGraw, a mechanical engineer and indoor air quality expert, uses numerical equations powered by high-performance computing to analyze and solve problems related to the dispersion patterns of biological pathogens as well as chemical irritants in buildings. Credit: ORNL, U.S. Dept. of Energy

Long before COVID-19’s rapid transmission led to a worldwide pandemic, Oak Ridge National Laboratory’s Jason DeGraw was performing computer modeling to better understand the impact of virus-laden droplets on indoor air quality

An algorithm developed and field-tested by ORNL researchers uses machine learning to maintain homeowners’ preferred temperatures year-round while minimizing energy costs. Credit: ORNL, U.S. Dept. of Energy

Oak Ridge National Laboratory researchers designed and field-tested an algorithm that could help homeowners maintain comfortable temperatures year-round while minimizing utility costs.

An ORNL-led team comprising researchers from multiple DOE national laboratories is using artificial intelligence and computational screening techniques – in combination with experimental validation – to identify and design five promising drug therapy approaches to target the SARS-CoV-2 virus. Credit: Michelle Lehman/ORNL, U.S. Dept. of Energy

An ORNL-led team comprising researchers from multiple DOE national laboratories is using artificial intelligence and computational screening techniques – in combination with experimental validation – to identify and design five promising drug therapy approaches to target the SARS-CoV-2 virus.

Innovation Crossroads Cohort 5 includes left to right: Caleb Alexander, DayLyte Batteries; Sam Evans, Unbound Water Technologies; Tommy Gibbons, Hempitecture; Shuchi “SK” Khurana, Addiguru; Forrest Shriver, Sentinel Devices; and Philip Stuckey, FC Renew.

Six science and technology innovators from across the United States will join the fifth cohort of Oak Ridge National Laboratory’s Innovation Crossroads program in June.

ORNL researchers developed an innovative insulation system that uses sensors and controls to exchange heat or coolness between a building and its thermal energy storage system, which maximizes energy savings. Credit: Andrew Sproles and Michelle Lehman/ORNL, U.S. Dept. of Energy

Researchers at Oak Ridge National Laboratory have developed a novel envelope system that diverts heat or coolness away from a building and stores it for future use.

ORNL’s non-disruptive air leak detector captures air escaping from exterior walls and uses refractive imaging to calculate the leakage flow rate. Credit: ORNL, U.S. Dept. of Energy

A team of researchers at Oak Ridge National Laboratory has developed a method to detect and measure air leaking from a building’s walls and roof that is quicker, cheaper and less disruptive to occupants.

Belinda Akpa applies her diverse expertise and high-performance computing to accelerate the drug discovery process and increase the chances of success when candidate molecules go to clinical trials. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy

Belinda Akpa is a chemical engineer with a talent for tackling big challenges and fostering inclusivity and diversity in the next generation of scientists.

Oak Ridge National Laboratory’s MENNDL AI software system can design thousands of neural networks in a matter of hours. One example uses a driving simulator to evaluate a network’s ability to perceive objects under various lighting conditions. Credit: ORNL, U.S. Dept. of Energy

The Department of Energy’s Oak Ridge National Laboratory has licensed its award-winning artificial intelligence software system, the Multinode Evolutionary Neural Networks for Deep Learning, to General Motors for use in vehicle technology and design.

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The U.S. Department of Energy’s Innovative and Novel Computational Impact on Theory and Experiment, or INCITE, program is seeking proposals for high-impact, computationally intensive research campaigns in a broad array of science, engineering and computer science domains.