A First-Principles Study on Na and O Adsorption Behaviors on Mo (110) Surface

Molybdenum-rhenium alloys are usually used as the wall materials for high-temperature heat pipes using liquid sodium as heat-transfer medium. The corrosion of Mo in liquid Na is a key challenge for heat pipes. In addition, oxygen impurity also plays an important role in affecting the alloy resistance to Na liquid. In this article, the adsorption and diffusion behaviors of Na atom on Mo (110) surface are theoretically studied using first-principles approach, and the effects of alloy Re and impurity O atoms are investigated. The result shows that the Re alloy atom can strengthen the attractive interactions between Na/O and the Mo substrate, and the existence of Na or O atom on the Mo surface can slower down the Na diffusion by increasing diffusion barrier. The surface vacancy formation energy is also calculated. For the Mo (110) surface, the Na/O co-adsorption can lead to a low vacancy formation energy of 0.47 eV, which indicates the dissolution of Mo is a potential corrosion mechanism in the liquid Na environment with O impurities. It is worth noting that Re substitution atom can protect the Mo surface by increasing the vacancy formation energy to 1.06 eV.

Predicting Perovskite Performance with Multiple Machine-Learning Algorithms

Perovskites have attracted increasing attention because of their excellent physical and chemical properties in various fields, exhibiting a universal formula of ABO3 with matching compatible sizes of A-site and B-site cations. In this work, four different prediction models of machine learning algorithms, including support vector regression based on radial basis kernel function (SVM-RBF), ridge regression (RR), random forest (RF), and back propagation neural network (BPNN), are established to predict the formation energy, thermodynamic stability, crystal volume, and oxygen vacancy formation energy of perovskite materials. Combined with the fitting diagrams of the predicted values and DFT calculated values, the results show that SVM-RBF has a smaller bias in predicting the crystal volume. RR has a smaller bias in predicting the thermodynamic stability. RF has a smaller bias in predicting the formation energy, crystal volume, and thermodynamic stability. BPNN has a smaller bias in predicting the formation energy, thermodynamic stability, crystal volume, and oxygen vacancy formation energy. Obviously, different machine learning algorithms exhibit different sensitivity to data sample distribution, indicating that we should select different algorithms to predict different performance parameters of perovskite materials.

Vacancy Formation Energy and Kinetic Properties of Liquid Metals

We investigated the relationship of the vacancy formation energy with kinematic viscosity and self-diffusion coefficient in liquid metals at the melting temperature. Formulas are obtained that relate experimental values of the vacancy formation energy, kinematic viscosity, and self-diffusion coefficient to the atomic size and mass, the melting and Debye temperatures. The viscosity and self-diffusion parameters are introduced. The ratio of these parameters to vacancy formation energy is equal to dimensionless constants. It is shown that the formulas for viscosity and self-diffusion differ only in dimensionless constants; the values of these constants are calculated. Linear regression analysis was carried out and formulas with the highest adjusted coefficient of determination were identified. The calculated values of the self-diffusion coefficient for a large number of liquid metals are presented.

Data-driven machine learning model for the prediction of oxygen vacancy formation energy of metal oxide materials

Metal oxides are widely used in the fields of chemistry, physics and materials. Oxygen vacancy formation energy is a key parameter to describe the chemical, mechanical, and thermodynamic properties of...

Formation of oxygen vacancies in Li2FeSiO4: first-principles calculations

The oxygen vacancy (left panel) and the vacancy formation energy as a function of temperature and pressure (right panel).

Factors determining surface oxygen vacancy formation energy in ternary spinel structure oxides with zinc

Spinel oxides are an important class of materials for heterogeneous catalysis including photocatalysis and electrocatalysis. The surface O vacancy formation energy (EOvac) is a critical quantity on catalyst performance because...