首页|University of Waterloo Reports Findings in Machine Learning (Computational and M achine Learning Methods for CO2 Capture Using Metal-Organic Frameworks)
University of Waterloo Reports Findings in Machine Learning (Computational and M achine Learning Methods for CO2 Capture Using Metal-Organic Frameworks)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – New research on Machine Learning is the subject o f a report. According to news reporting originating in Waterloo, Canada, by News Rx journalists, research stated, “Machine learning (ML) using data sets of atomi c and molecular force fields (FFs) has made significant progress and provided be nefits in the fields of chemistry and material science. This work examines the i nteractions between chemistry and materials computational science at the atomic and molecular scales for metal-organic framework (MOF) adsorbent development tow ard carbon dioxide (CO) capture.” The news reporters obtained a quote from the research from the University of Wat erloo, “Herein, a connection will be drawn between atomic forces predicted by ML algorithms and the structures of MOFs for CO adsorption. Our study also takes i nto account the successes of atomic computational screening in the field of mate rials science, especially quantum ML, and its relationship to ML algorithms that clarify advancements in the area of CO adsorption by MOFs. Additionally, we rev iewed the processes for supplying data to ML algorithms for algorithm training, including text mining from scientific articles, and MOF’s formula processing lin ked to the chemical properties of MOFs. To create ML algorithms for future resea rch, we recommend that the digitization of scientific records can help efficient ly synthesize advanced MOFs.”
WaterlooCanadaNorth and Central Amer icaAlgorithmsChemistryCyborgsEmerging TechnologiesMachine Learning
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