We have used density-functional theory to investigate (111), (110), (210), (211), (100), and (310) surfaces of ceria (CeO2). Compared with previous interatomic-potential-based studies, our calculations reported a slightly different relative stability ordering and significantly lower surface energies for the stoichiometric surfaces. Using a defect model, the surface stabilities were evaluated as functions of oxygen partial pressure and temperature. Our investigations were restricted to ideal surface terminations, without considering defect formation on those surfaces. We found that at 300 K, the stoichiometric (111) has the lowest free energy for a wide range of oxygen partial pressures up to 1 atm, and only at ultrahigh vacuum does the Ce-terminated (111) becomes the most stable one. The transition point for the Ce-terminated (111) surfaces moves to higher oxygen partial pressures when temperature increases. To improve the prediction of electron density of states, we used the local-density approximation plus U(J) correction method to correct the on-site Coulomb correlation and exchange interaction due to the strongly localized Ce-4f electrons. The optimal parameter combination of U = 7 eV and J = 0.7 eV was found to improve the O 2p-Ce 4f gap without much degradation of ground-state bulk properties or the O 2p-Ce 5d gap. The bulk and surface electronic structures were then analyzed based on the improved density of states.

Converting CO2 into carbon‐based fuels is promising for relieving the greenhouse gas effect and the energy crisis. However, the selectivity and efficiency of current electrocatalysts for CO2 reductions are still not satisfactory. In this paper, the development of machine learning methods in screening CO2 reduction electrocatalysts over the recent years is reviewed. Through high‐throughput calculation of some key descriptors such as adsorption energies, d‐band center, and coordination number by well‐constructed machine learning models, the catalytic activity, optimal composition, active sites, and CO2 reduction reaction pathway over various possible materials can be predicted and understood. Machine learning is now realized as a fast and low‐cost method to effectively explore high performance electrocatalysts for CO2 reduction.

Density functional methods were used to study the environmental dependence of O vacancy formation in CeO2. It was found that an O vacancy in the 2+ charged state has the lowest formation energy for a wide range of Fermi energies (EF) from 0 to ∼1.9eV, while a neutral vacancy becomes the most stable at higher EF values. The O vacancy formation energy can be strongly affected by temperature (T) and the partial pressure of oxygen (PO2). The effect of T and PO2 on the equilibrium compositions of reduced ceria (CeO2−x) was also calculated and showed qualitative agreement with gravimetric experiments.

Density-functional theory ͑DFT͒ calculations have been performed to study the atomic scale energies, structures, and formation mechanisms of dissolved Y, Ti, and O solutes and small Y-Ti-O nanoclusters ͑NCs͒ in a bcc Fe lattice. Key results include the following observations. The Y and O are dissolved during mechanical alloying of Y 2 O 3 with metal powders by ball milling, which provides the large requisite solution energy of about 4 eV per yttria atom. The solution energies of substitutional Y as well as interstitial O, which are referenced to elemental Y and solid FeO, respectively, are also high. Bound O-O, Y-O, and Ti-O pairs decrease the system energy relative to the isolated solutes and constitute the basic building blocks for NCs. The lowest energy configuration for a Y 2 TiO 3 NC is 5.11 eV less than the total energy of the dissolved solutes. Our DFT calculations show that Y-Ti-O NC formation can take place without the energetic assistance of pre-existing vacancies. This conclusion is significant since excess vacancies are not a persistent thermodynamic-energetic constituent of the Fe-Y-Ti-O system and will quickly annihilate at dislocations during high-temperature powder consolidation.

Pyrochlore Y2Ti2O7 is a primary precipitate phase in nano-structured ferritic alloys (NFAs) for fission and fusion energy applications. We report a theoretical study for assessing the relative stability of trapping helium in Y2Ti2O7 versus in matrix iron. Various defect structures and the associated energies are examined and compared. Results reveal that helium can be deeply trapped in Y2Ti2O7 and that the corresponding self-interaction is essentially repulsive. Transmutant helium in NFAs prefers to occupy individual octa-interstitial sites in Y2Ti2O7, before forming small clusters in Y2Ti2O7. Helium partitioning in NFAs depends on the number and dispersion of Y2Ti2O7; and thus initially, bubble formation and growth in iron matrix can be largely suppressed. Charge transfer occurs from helium to neighboring oxygen anions, but not to neighboring metal cations, suggesting a general effectiveness of trapping helium in oxides. Reasons for the ultimate fate of helium to form small nm-scale interface bubbles are also discussed.

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Nano-size Y-Ti-oxides are largely responsible for the extraordinary mechanical properties and irradiation tolerance of nano-structured ferritic alloys (NFAs). Here we report a theoretical study to assess the characters and possible roles of the ferrite/oxide interface in managing neutron transmutation product helium in NFAs. Using one observed cube-on-cube orientation relationship, various candidate structures of the ferrite/Y2Ti2O7 interfaces were constructed and the associated energies were carefully evaluated. The interface phase diagram is obtained by expressing the energy as a function of temperature and internal oxygen activity (expressed in terms of oxygen partial pressure). The oxide interfaces are predicted to be Y/Ti-rich at thermodynamic equilibrium for the wide temperature range of interest. Vacancy formation energies are lower and helium segregates to the interfaces, in preference to the iron matrix and grain boundaries. Combined with our previous results on bulk-phase Y2Ti2O7, the profound implications of nano-oxides to helium management in NFAs are discussed.

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