首页|Studies from National Research Council (CNR) Yield New Data on Machine Learning (Machine Learning Unveils the Physical Properties of Materials Driving Thermoele ctric Generator Efficiency: the Case of Half-heuslers)
Studies from National Research Council (CNR) Yield New Data on Machine Learning (Machine Learning Unveils the Physical Properties of Materials Driving Thermoele ctric Generator Efficiency: the Case of Half-heuslers)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News - Investigators publish new report on Ma chine Learning. According to news reportingfrom Bologna, Italy, by NewsRx edito rs, the research stated, “We report a machine learning (ML)-basedapproach allow ing thermoelectric generator (TEG) efficiency evaluation directly from five para meters:two physical properties-carrier density and energy gap, and three engine ering parameters-external loadresistance, TEG hot side temperature, and leg hei ght. Then, we use a genetic algorithm to optimize theseparameters to maximize T EG efficiency.”
BolognaItalyEuropeCyborgsEmergin g TechnologiesEngineeringMachine LearningNational Research Council (CNR)
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