Accurate understanding of structural integrity and chemical reactivity of UO2 disposed in deep underground sites is of importance. Owing to the specific condition of the site location, UO2 may have substantially different properties from the conventional prediction. In this study, we demonstrate that the oxidation resistivity of UO2 is considerably modified by gadolinium (Gd), which is the element of neutron absorber and a byproduct of nuclear decay of radioactive U-235. Using density functional theory calculations, we investigate how the oxidation mechanism of UO2 changes with Gd incorporation in U lattice. Our study indicates that Gd remarkably enhances the thermodynamic stability of pristine UO2 against surface oxidation via three underlying mechanisms: (i) weakens the chemical bonding of adsorbed oxygen atom (O) with U, (ii) reduces active sites (U) for oxygen adsorption, and (iii) suppresses the subsurface diffusion of adsorbed O delaying the growth of the oxide layers on the UO2. Electronic and lattice structure analyses for Gd-doped UO2 indicate that amount of charge transfer from U to O is critically reduced and the lattice of the UO2 surface is contracted. Our results provide useful information for understanding long-term stability and improving the structural integrity of UO2 through the chemical doping process.

The success of machine learning approach to identify key correlation in large database is critically controlled by the reliability and accuracy of the data. Here, we demonstrate that rigorous material properties of radioactive nuclear fuels can be obtained by integrated approach of first principles calculations and the machine learning approach. The reliable database is established by density functional theory and molecular dynamics simulations, which is the input of the machine learning to analyze any correlation among the database. The outcomes are applied to evaluate thermodynamic, kinetic and electrochemical properties, which plays a key role for safe management of spent nuclear fuels.