首页|Investigators at SLAC National Accelerator Laboratory Discuss Findings in Machin e Learning (Machine Learning for Accuracy In Density Functional Approximations)
Investigators at SLAC National Accelerator Laboratory Discuss Findings in Machin e Learning (Machine Learning for Accuracy In Density Functional Approximations)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Machine Learning is th e subject of a report. According to news reporting from Menlo Park, California, by NewsRx editors, the research stated, “Machine learning techniques have found their way into computational chemistry as indispensable tools to accelerate atom istic simulations and materials design. In addition, machine learning approaches hold the potential to boost the predictive power of computationally efficient e lectronic structure methods, such as density functional theory, to chemical accu racy and to correct for fundamental errors in density functional approaches.” Funders for this research include United States Department of Energy (DOE), Unit ed States Department of Energy (DOE).
Menlo ParkCaliforniaUnited StatesN orth and Central AmericaCyborgsEmerging TechnologiesMachine LearningSLAC National Accelerator Laboratory
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