Robotics & Machine Learning Daily News2024,Issue(Feb.1) :38-39.DOI:10.1063/5.0180541

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)

Robotics & Machine Learning Daily News2024,Issue(Feb.1) :38-39.DOI:10.1063/5.0180541

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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Abstract

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.

Key words

Atlanta/Georgia/United States/North and Central America/Cyborgs/Emerging Technologies/Machine Learning/Georgia Institute of Technology

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出版年

2024
Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

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被引量3
参考文献量94
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