New Machine Learning Findings Reported from Aarhus University [Cascading Symmetry Constraint During Machine Learning-enabled Structural Search for Sulfur-induced Cu(111)-( 43 X 43) Surface Reconstruction]
New Machine Learning Findings Reported from Aarhus University [Cascading Symmetry Constraint During Machine Learning-enabled Structural Search for Sulfur-induced Cu(111)-( 43 X 43) Surface Reconstruction]
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 originating from Aarhus, Denm ark, by NewsRx correspondents, research stated, "In this work, we investigate ho w exploiting symmetry when creating and modifying structural models may speed up global atomistic structure optimization." Financial supporters for this research include Danmarks Grundforskningsfond, Vil lum Fonden through Investigator Grant, Danish National Research Foundation throu gh the Center of Excellence "InterCat."Our news editors obtained a quote from the research from Aarhus University, "We propose a search strategy in which models start from high symmetry configuration s and then gradually evolve into lower symmetry models. The algorithm is named c ascading symmetry search and is shown to be highly efficient for a number of kno wn surface reconstructions."
Key words
Aarhus/Denmark/Europe/Chalcogens/Cyb orgs/Emerging Technologies/Machine Learning/Sulfur/Aarhus University
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