首页|Researchers from Tsinghua University Report Findings in Machine Learning (Transf erable Performance of Machine Learning Potentials Across Graphene-water Systems of Different Sizes: Insights From Numerical Metrics and Physical Characteristics )
Researchers from Tsinghua University Report Findings in Machine Learning (Transf erable Performance of Machine Learning Potentials Across Graphene-water Systems of Different Sizes: Insights From Numerical Metrics and Physical Characteristics )
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News - A new study on Machine Learning is now available. According to news reporting outof Beijing, People’s Republic of Chi na, by NewsRx editors, research stated, “Machine learning potentials(MLPs) are promising for various chemical systems, but their complexity and lack of physica l interpretabilitychallenge their broad applicability. This study evaluates the transferability of the deep potential (DP) andneural equivariant interatomic p otential (NequIP) models for graphene-water systems using numericalmetrics and physical characteristics.”
BeijingPeople’s Republic of ChinaAsi aCyborgsEmerging TechnologiesMachine LearningTsinghua University
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