Reports from Georgia Institute of Technology Highlight Recent Findings in Machin e Learning (Machine Learning and Iast-aided High-throughput Screening of Cationi c and Silica Zeolites for Alkane Capture, Storage, and Separations)
Reports from Georgia Institute of Technology Highlight Recent Findings in Machin e Learning (Machine Learning and Iast-aided High-throughput Screening of Cationi c and Silica Zeolites for Alkane Capture, Storage, and Separations)
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Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – Current study results on Machine Learn ing have been published. According to newsreporting originating in Atlanta, Geo rgia, by NewsRx journalists, research stated, “We present an approachfor quanti tatively predicting the temperature-dependent single-component adsorption behavi or of linearalkanes in silica and Na-exchanged cationic zeolites using machine learning (ML) models trained fromextensive molecular simulations based on force fields with coupled cluster accuracy. A high-performingclassification model wa s developed to distinguish between instances with negligible and non-negligibleadsorption.”
Key words
Atlanta/Georgia/United States/North a nd Central America/Aluminum Silicates/Cyborgs/Emerging Technologies/Inorgani c Chemicals/Machine Learning/Oxides/Oxygen Compounds/Silicic Acid/Silicon C ompounds/Silicon Dioxide/Zeolites/Georgia Institute of Technology
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