首页|Findings on Machine Learning Discussed by Investigators at National Energy Techn ology Laboratory (High-throughput Ab Initio Calculations and Machine Learning To Discover Srfeo3-6-based Perovskites for Chemical-looping Applications)
Findings on Machine Learning Discussed by Investigators at National Energy Techn ology Laboratory (High-throughput Ab Initio Calculations and Machine Learning To Discover Srfeo3-6-based Perovskites for Chemical-looping Applications)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Fresh data on Machine Learning are pre sented in a new report. According to news reporting originating in Pittsburgh, P ennsylvania, by NewsRx journalists, research stated, “Design of highperformance oxygen carrier materials plays a crucial role in chemical -looping applications . Perovskite-type ABO3 oxides have received significant attention due to their h igh thermal and mechanical stability and high oxygen mobility.” Financial supporters for this research include United States Department of Energ y (DOE), National Energy Technology Laboratory (NETL) Research & I nnovation Center’s Advanced Reaction Systems, NETL Advanced Reaction Systems fie ld work proposal (FWP).
PittsburghPennsylvaniaUnited StatesNorth and Central AmericaChalcogensCyborgsEmerging TechnologiesMachine LearningNational Energy Technology Laboratory
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