首页|New Findings from University of Electronic Science and Technology of China Describe Advances in Machine Learning (Hydrogen Diffusion In Zirconium Hydrides From On-the-fly Machine Learning Molecular Dynamics)
New Findings from University of Electronic Science and Technology of China Describe Advances in Machine Learning (Hydrogen Diffusion In Zirconium Hydrides From On-the-fly Machine Learning Molecular Dynamics)
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NETL
NSTL
Elsevier
Fresh data on Machine Learning are presented in a new report. According to news reporting from Chengdu, People’s Republic of China, by NewsRx journalists, research stated, “Reactor pressure vessels and fuel cladding tubes have repeatedly failed due to zirconium hydrides. Zirconium hydride precipitation and growth are directly affected by hydrogen atom transport properties, which would make nuclear fuel storage less safe over long periods of time.” Financial supporters for this research include National Natural Science Foundation of China (NSFC), Key Project of National Natural Science Foundation of China -China Academy of Engineering Physics joint Foundation (NSAF). The news correspondents obtained a quote from the research from the University of Electronic Science and Technology of China, “Herein, we employ first-principles calculations to investigate the hydrogen diffusion mechanism in zirconium hydrides, utilizing on-the-fly machine learning force field molecular dynamics. It is verified that the machine learning force field can accurately describe the hydrogen atomic diffusion properties in zirconium hydrides at several temperatures and compositions. The atomic migration paths of hydrogen in zirconium hydrides as well as their barriers and pre-factors are also calculated.”
ChengduPeople’s Republic of ChinaAsiaCyborgsElementsEmerging TechnologiesGasesHydrogenInorganic ChemicalsMachine LearningMolecular DynamicsPhysicsTransition ElementsZirconiumUniversity of Electronic Science and Technology of China
NETL
NSTL
Elsevier
