Study Findings from Vanderbilt University Provide New Insights into Machine Lear ning (Dft and Machine Learning for Predicting Hydrogen Adsorption Energies On Ro cksalt Complex Oxides)
Study Findings from Vanderbilt University Provide New Insights into Machine Lear ning (Dft and Machine Learning for Predicting Hydrogen Adsorption Energies On Ro cksalt Complex Oxides)
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators publish new report on Ma chine Learning. According to news originating from Nashville, Tennessee, by News Rx editors, the research stated, “The prediction of hydrogen adsorption energies on complex oxides by integrating DFT calculations and machine learning is consi dered. In particular, 14 descriptors for electronic and geometric properties eva luation are adapted within a 336 hydrogen adsorption energy dataset created.” Financial support for this research came from Vanderbilt University for the Post doctoral Fellowship.Our news journalists obtained a quote from the research from Vanderbilt Universi ty, “Supervised learning techniques were explored to establish an accurate predi ctive model. With the deep neural network results, a MAE of about 0.06 eV is ach ieved.”
Key words
Nashville/Tennessee/United States/Nor th and Central America/Anions/Cyborgs/Elements/Emerging Technologies/Gases/Hydrogen/Inorganic Chemicals/Machine Learning/Oxides/Oxygen Compounds/Vand erbilt University
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