NiFe2O4 is a key component of CRUD (corrosion-related undesirable deposits) that may severely degrade the safety and efficiency of nuclear power systems. Recent experimental studies showed that Cr impurity may affect the formation and growth of NiFe2O4-rich deposit on Cr-coated nuclear fuel, demonstrating a promising solution to inhibit the growth of CRUD. However, little theoretical study has been dedicated to revealing the effect of impurity on the formation and growth of NiFe2O4, especially Cr. In this work, density functional theory was utilized to compute the effect of Cr doping on electronic structures of NiFe2O4. The calculation results suggested that the effect of Cr impurity on NiFe2O4 structural stability is site-dependent and provides theoretical evidence that explains the experimental observation of suppressed NiFe2O4-rich CRUD growth on Cr-coated nuclear fuel. We believe that lattice mismatch induced by Cr doping may lead to stress and dislocations, which suppressed further growth of NiFe2O4. Subsequently, the diffusion of Cr facilitates the smooth growth of the interface, suggesting that the diffusion of Cr may be a crucial factor influencing the growth rate of CRUD.

Doped perovskites are widely studied in the domain of perovskite material design. However, due to the limited data available for the target materials, machine learning methods based on small datasets become particularly important. In this study, we propose a transfer learning strategy aimed at predicting doped perovskites on limited data samples. This strategy first utilizes the ABO₃-type perovskite dataset to develop a deep learning source model based on its formation energies. Then, fine-tuning is performed on the doped perovskite structure dataset to obtain a model with good transferability, applicable to the doped perovskite oxide target domain. Based on the transfer learning model, we further predict the formation energies of 12,897 A₂BB′O₆ compounds, 10,401 AA′B₂O₆ compounds, and 49,723 AA′BB′O₆ compounds. With the tolerance factor $\mathrm{t} \in [0.7–1.1]$, octahedral factor $\mathsf{\mu } \in [0.45–0.7]$, and the modified tolerance factor $\mathsf{\tau } \in [0, 4.18]$ for screening, we successfully predict 3389 A₂B′BO₆, 3002 AA′B₂O₆, and 13,563 AA′BB′O₆ structures as potential stable doped perovskite candidates. Among these filtered results, 821 A₂B′BO₆, 69 AA′B₂O₆, and 6 AA′BB′O₆ compounds have been reported in the OQMD database. For each doped perovskite, we select the candidate with the lowest formation energy and perform DFT validation. This resulted in three newly reported stable doped perovskite materials: CaSrHfScO₆, BaSrHf₂O₆, and Ba₂HfNdO₆. The transfer learning-based perovskite material design method proposed in this study not only effectively addresses the challenges of model training on small datasets but also significantly improves the accuracy and stability of doped perovskite material predictions. Through transfer learning, the model can fully leverage the data and knowledge from the ABO₃-type perovskite, effectively overcoming the problem of limited data. This strategy provides a new approach for efficient perovskite material design, enabling broader structural and performance predictions under limited experimental data conditions, and offering a powerful tool for the development of novel functional materials.

The discovery of cathode materials that simultaneously exhibit high oxygen-reduction activity, robust stability, and low cost is pivotal to moving solid oxide fuel cells (SOFCs) from the laboratory into commercial deployment. To address this challenge, we compile the largest perovskite dataset to date parameterized by the oxygen tracer surface exchange coefficient (k^*). Using only readily obtainable elemental and structural descriptors, we develop machine-learning models that surpass existing approaches in both accuracy and computational efficiency. Specifically, by integrating Mahalanobis-distance-based applicability-domain analysis with random forest-enhanced property descriptors and support vector regression, we high-throughput-screen 1.3 million ABO3 compositions and curate a candidate list that balances thermodynamic stability, cost, and oxygen-reduction activity. Beyond prediction accuracy, SHAP interpretation reveals strong physical correlations between the enhanced descriptors and k^*, highlighting the coefficient of thermal expansion, O p-band center, and A-site ionic radius as the dominant factors governing oxygen exchange kinetics. Finally, we identify 209 promising perovskite cathodes predicted to outperform LSC in the low-temperature regime, offering promising directions for experimental realization of practical low-temperature SOFCs.

Uranium mononitride (UN) is a promising nuclear fuel with a high melting point, high thermal conductivity, and low coefficient of thermal expansion. Theoretical studies of UN can provide insights on its thermal transport mechanism, which is of great significance for the design and application of UN fuel. During the processing and operation, crystal defects and impurities, such as vacancies and carbon impurities, potentially arise in the nuclear fuel, which probably affect the thermomechanical properties of UN. To figure out the effect of vacancy defects and carbon impurities on the thermal conductivity of UN, density functional theory and Boltzmann transport theory are applied to conduct a theoretical investigation on the mechanical and thermal properties of ideal and defective UN. The calculated results show that in the case of UN with a U or N vacancy, both the lattice and electronic thermal conductivity are decreased, compared with the ideal case. With a carbon atom occupying the N site in the lattice, the electronic thermal conductivity is reduced but the lattice thermal conductivity is increased. Combining the results of lattice and electronic thermal conductivity, the total thermal conductivities of three defective states are lower than the ideal UN. The thermal conductivities of UN with a U vacancy (13.91 W/mK), N vacancy (15.36 W/mK), and a carbon atom occupying the N site (15.14 W/mK) are, respectively, reduced by 25.7%, 18.0%, and 19.2%, in comparison with ideal result (18.73 W/mK) at 1000 K.