Authors: George Rajna
Researchers at the U.S. Department of Energy's (DOE) Princeton Plasma Physics Laboratory (PPPL) are using ML to create a model for rapid control of plasma-the state of matter composed of free electrons and atomic nuclei, or ions-that fuels fusion reactions.  Machine learning can be used to predict the properties of a group of materials which, according to some, could be as important to the 21st century as plastics were to the 20th.  Marculescu, along with ECE Ph.D. student Chieh Lo, has developed a machine learning algorithm-called MPLasso-that uses data to infer associations and interactions between microbes in the GI microbiome.  A team of researchers from the University of Muenster in Germany has now demonstrated that this combination is extremely well suited to planning chemical syntheses-so-called retrosyntheses-with unprecedented efficiency.  Two physicists at ETH Zurich and the Hebrew University of Jerusalem have developed a novel machine-learning algorithm that analyses large data sets describing a physical system and extract from them the essential information needed to understand the underlying physics. 
Comments: 34 Pages.
[v1] 2019-05-18 02:10:45
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