首页|Recent Studies from Beijing University of Technology Add New Data to Artificial Intelligence (Artificial Intelligence Driven Design of Cathode Materials for Sod ium-ion Batteries Using Graph Deep Learning Method)
Recent Studies from Beijing University of Technology Add New Data to Artificial Intelligence (Artificial Intelligence Driven Design of Cathode Materials for Sod ium-ion Batteries Using Graph Deep Learning Method)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – Data detailed on Artificial Intelligen ce have been presented. According to newsreporting from Beijing, People’s Repub lic of China, by NewsRx journalists, research stated, “While deeplearning has b een used in battery computing to speed up the search for new cathode materials, the majorityof deep learning techniques only take into account elemental inform ation and topological information,ignoring the significance of geometrical info rmation and global information for electrode average voltageprediction. Multiva lent metal-ion Battery Voltage Graph Neural Network (MBVGNN) proposed in presentwork, which combines global information and geometric information.”
BeijingPeople’s Republic of ChinaAsi aArtificial IntelligenceEmerging TechnologiesMachine LearningBeijing Uni versity of Technology
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