首页|Findings from Georgia Institute of Technology Yields New Data on Machine Learning (Kohn-sham Accuracy From Orbital-free Density Functional Theory Via D-machine Learning)
Findings from Georgia Institute of Technology Yields New Data on Machine Learning (Kohn-sham Accuracy From Orbital-free Density Functional Theory Via D-machine Learning)
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Data detailed on Machine Learning have been presented. According to news reporting from Atlanta, Georgia, by NewsRx journalists, research stated, “We present a Delta-machine learning model for obtaining Kohn-Sham accuracy from orbital-free density functional theory (DFT) calculations. In particular, we employ a machine-learned force field (MLFF) scheme based on the kernel method to capture the difference between Kohn-Sham and orbital-free DFT energies/forces.” Funders for this research include United States Department of Energy (DOE), United States Department of Energy (DOE), United States Department of Energy (DOE), Advanced Computing Environment (PACE) through its Hive (U.S. National Science Foundation), Phoenix clusters at Georgia Institute of Technology, Atlanta, Georgia.
AtlantaGeorgiaUnited StatesNorth and Central AmericaCyborgsEmerging TechnologiesMachine LearningGeorgia Institute of Technology
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