首页|Research Data from Los Alamos National Laboratory Update Understanding of Machin e Learning (Building a DFT+U machine learning interatomic potential for uranium dioxide)
Research Data from Los Alamos National Laboratory Update Understanding of Machin e Learning (Building a DFT+U machine learning interatomic potential for uranium dioxide)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators discuss new findings in artificial intelligence. According to news reporting originating from Los Alamos , New Mexico, by NewsRx correspondents, research stated, "Despite uranium dioxid e (UO2) being a widely used nuclear fuel, fuel performance models rely extensive ly on empirical correlations of material behavior, leveraging the historical ope rating experience of UO2. Mechanistic models that consider an atomistic understa nding of the processes governing fuel performance (such as fission gas release a nd creep) will enable a better description of fuel behavior under non-prototypic al conditions such as in new reactor concepts or for modified UO2 fuel compositi ons." Our news journalists obtained a quote from the research from Los Alamos National Laboratory: "To this end, molecular dynamics simulation is a powerful tool for rapidly predicting physical properties of proposed fuel candidates. However, the reliability of these simulations depends largely on the accuracy of the atomic forces. Traditionally, these forces are computed using either a classical force field (FF) or density functional theory (DFT). While DFT is relatively accurate, the computational cost is burdensome, especially for f-electron elements, such as actinides. By contrast, classical FFs are computationally efficient but are l ess accurate. For these reasons, we report a new accurate machine learning inter atomic potential (MLIP) for UO2 that provides high-fidelity reproduction of DFT forces at a similar low cost to classical FFs. We employ an active learning appr oach that autonomously augments the DFT training data set to iteratively refine the MLIP."
Los Alamos National LaboratoryLos Alam osNew MexicoUnited StatesNorth and Central AmericaActinoid Series Elemen tsCyborgsEmerging TechnologiesMachine LearningUranium
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