首页|Report Summarizes Machine Learning Study Findings from George Washington Univers ity (Intricate Short-range Order In Gesn Alloys Revealed By Atomistic Simulation s With Highly Accurate and Efficient Machine-learning Potentials)
Report Summarizes Machine Learning Study Findings from George Washington Univers ity (Intricate Short-range Order In Gesn Alloys Revealed By Atomistic Simulation s With Highly Accurate and Efficient Machine-learning Potentials)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Current study results on Machine Learn ing have been published. According to news reporting out of Washington, District of Columbia, by NewsRx editors, research stated, “GeSn alloys hold promise for silicon-compatible integrated applications in electronics, photonics, and topolo gical quantum devices. However, understanding their intricate structures using d ensity functional theory (DFT) calculations is hindered by spatiotemporal constr aints.” Financial supporters for this research include United States Department of Energ y (DOE), United States Department of Energy (DOE), NERSC.
WashingtonDistrict of ColumbiaUnited StatesNorth and Central AmericaAlloysCyborgsEmerging TechnologiesMach ine LearningGeorge Washington University
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