Michel Freyss1 , Boris Dorado1,2 , Marjorie Bertolus1 , G´erald Jomard1 , Emerson Vathonne1 , Philippe Garcia1 , Bernard Amadon2
1 CEA, DEN, Centre de Cadarache, DEC/SESC/LLCC, 13108 Saint-Paul lez Durance, France
2 CEA, DAM, DIF, 91297 Arpajon, France
Abstract:
The present highlight reviews recent advances in first-principles modelling of radiation damage in UO2. It focuses on the influence of strong correlations and the problem of metastable states that occur with some approximations that localize electrons, in particular the density functional theory (DFT)+U approximation. It gives an illustration that DFT+U calculations quantitatively describe atomic transport phenomena in strongly-correlated uranium dioxide, provided that one circumvents the DFT+U local energy minima issue that affects f-electron systems. The occupation matrix control (OMC) scheme is one of the techniques developed to tackle the metastable state issue. We demonstrate here its efficiency on perfect and defective UO2 through the study of oxygen diffusion. We use OMC to calculate UO2 bulk properties, defect formation energies, migration energy barriers, and we show that in order to avoid the metastable states and systematically reach the ground state of uranium dioxide with DFT+U, the monitoring of occupation matrices of the correlated orbitals on which the Hubbard term is applied is crucial. The presence of metastable states can induce significant differences in the calculated total energies, which explains the origin of the discrepancies in the results obtained by various authors on crystalline and defect-containing UO2. Also, for the bulk fluorite structure of UO2, we show that the widely used Dudarev approach of the DFT+U systematically yields the first metastable state when no control is done on the orbital occupancies. As for oxygen diffusion, the calculated migration energy relating to the interstitialcy mechanism compares very favourably to experimental data. Also, vacancy migration and Frenkel pair formation energies are shown to agree well with existing data.
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