Low-hysteresis shape-memory ceramics designed by multimode modelling.

Zirconia ceramics exhibit a martensitic phase transformation that enables large strains of order 10%, making them prospects for shape-memory and superelastic applications at high temperature. Similarly to other martensitic materials, this transformation strain can be engineered by carefully alloying to produce a more commensurate transformation with reduced hysteresis (difference in transformation temperature on heating and cooling). However, such 'lattice engineering' in zirconia is complicated by additional physical constraints: there is a secondary need to manage a large transformation volume change, and to achieve transformation temperatures high enough to avoid kinetic barriers.

Structural stability of Co-V intermetallic phases and thermodynamic description of the Co-V system.

The Co-V system has been reviewed. Density functional theory (DFT) calculations using the generalized gradient approximation (GGA) were used to obtain the energies for the end-members for all three intermediate phases, CoV, σ and CoV. Results from DFT calculations considering spin polarization were used to evaluate the CALPHAD (Calculation of phase diagrams) model parameters.

Thermodynamic analysis of the topologically close packed σ phase in the Co-Cr system.

Density functional theory (DFT) calculations show that it is essential to consider the magnetic contribution to the total energy for the end-members of the σ phase. A more straightforward method to use the DFT results in a CALPHAD (Calculation of phase diagrams) description has been applied in the present work. It was found that only the results from DFT calculations considering spin-polarization are necessary to obtain a reliable description of the σ phase.

Thermodynamic assessment of the Co-Ta system.

The Co-Ta system has been reviewed and the thermodynamic description was re-assessed in the present work. DFT (density functional theory) calculations considering spin polarization were performed to obtain the energies for all end-member configurations of the C14, C15, C36 and μ phases for the evaluation of the Gibbs energies of these phases. The phase diagram calculated with the present description agrees well with the experimental and theoretical data.

Prediction of seebeck coefficient for compounds without restriction to fixed stoichiometry: A machine learning approach.

The regression model-based tool is developed for predicting the Seebeck coefficient of crystalline materials in the temperature range from 300 K to 1000 K. The tool accounts for the single crystal versus polycrystalline nature of the compound, the production method, and properties of the constituent elements in the chemical formula. We introduce new descriptive features of crystalline materials relevant for the prediction the Seebeck coefficient.