首页|Researchers from Beijing University of Chemical Technology Describe Findings in Machine Learning (Lattice Thermal Conductivity of Solid Lif Based On Machine Lea rning Force Fields and the Greenkubo Approach)
Researchers from Beijing University of Chemical Technology Describe Findings in Machine Learning (Lattice Thermal Conductivity of Solid Lif Based On Machine Lea rning Force Fields and the Greenkubo Approach)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Current study results on Machine Learning have be en published. According to news reporting out of Beijing, People’s Republic of C hina, by NewsRx editors, research stated, “Obtaining accurate lattice thermal co nductivity data of LiF under extreme conditions not only provides important refe rence for performance evaluation, prediction, and control of materials, but also helps to alleviate the significant challenges of precise experimental measureme nts. The high-temperature phonon properties and lattice thermal conductivity (LT C) of solid LiF were calculated by combining on-the-fly machine learning force f ields (MLFFs) with the Green-Kubo method.” Funders for this research include National Natural Science Foundation of China ( NSFC), National Natural Science Foundation of China (NSFC).
BeijingPeople’s Republic of ChinaAsi aCyborgsEmerging TechnologiesMachine LearningMolecular DynamicsPhysicsBeijing University of Chemical Technology
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