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Northwestern University researchers have developed a new framework using machine learning that improves the accuracy of interatomic potentials the guiding rules describing how atoms interact in new materials design. The findings could lead to more accurate predictions of how new materials transfer heat, deform, and fail at the atomic scale.
Designing new nanomaterials is an important aspect of developing next-generation devices used in electronics, sensors, energy harvesting and storage, optical detectors, and structural materials. To design these materials, researchers create interatomic potentials through atomistic modeling, a computational approach that predicts how these materials behave by accounting for their properties at the smallest level. The process to establish materials interatomic potential called parameterization has required significant chemical and physical intuition, leading to less accurate prediction of new materials design.
DOE Nanoscale Science Research Centers Support QIS Research to Advance Energy, Economic, and National Security
DOE/US Department of Energy
WASHINGTON, D.C. - The U.S. Department of Energy (DOE) today announced plans to provide $30 million for Quantum Information Science (QIS) research that helps scientists understand how nature works on an extremely small scale 100,000 times smaller than the diameter of a human hair. QIS can help our nation solve some of the most pressing and complex challenges of the 21st century, from climate change to national security. Watch this video to learn more about QIS. Quantum computing and devices are poised to revolutionize the way we process information and develop new technologies that are currently beyond our reach, said Secretary of Energy Jennifer M. Granholm. From developing novel materials to building better batteries to moving clean electricity across the country more efficiently, the field of quantum information sciences can help us accel
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