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In the present work, the ideal shear strength ({\tau}_is) of dilute Ni34XZ ternary alloys (X or Z = Al, Co, Cr, Fe, Mn, Mo, Nb, Si, Ti) are predicted by first-principles calculations based on density functional theory (DFT) in terms of pure alias shear deformations. The {\tau}_is results show that within the concentration up to 8.3% of alloying elements, {\tau}_is increases with composition in binary systems with Mn, Fe, and Co in ascending order, and decreases with composition with Nb, Si, Mo, Ti, Al, and Cr in descending order. The composition dependence of {\tau}_is in binary and ternary systems is modeled using the CALculation of PHAse Diagrams (CALPHAD) approach considering lattice instability, indicating that atomic bonding strength significantly influences {\tau}_is. Correlational analyses further show that lattice constant and elastic constant C11 affect {\tau}_is, the most out of the elemental features.

In the present study, the DFT+U method was employed to predict the thermodynamic properties of Cr2O3, Nb2O5, and Ta2O5. Results were benchmarked with experimental data showing high accuracy, except for the negative thermal expansion (NTE) of Nb2O5, which is attributed to its polymorphic complexity. Additionally, we extended our analysis to rutile-type oxides CrNbO4 and CrTaO4, examining their entropy and heat capacity at finite temperatures. CrNbO4 displayed slightly higher entropy and heat capacity at high temperatures. The mean linear thermal expansion coefficients for CrNbO4 and CrTaO4 from 500 K to 2000 K were predicted to be 6.00*10-6/K and 13.49*10-6/K, respectively, corroborating with DFT predictions and experimental evidence. Our research highlights the precision of the DFT+U and phonon methods in predicting the thermodynamic properties of oxide materials, offering insights into the design of corrosion-resistant materials.

Knowing oxide-forming ability is vital to gain desired or avoid deleterious oxides formation through tuning oxidizing environment and materials chemistry. Here, we have conducted a comprehensive thermodynamic analysis of 137 binary oxides using the presently predicted Ellingham diagrams. It is found that the active elements to form oxides easily are the f-block elements (lanthanides and actinides), elements in the groups II, III, and IV (alkaline earth, Sc, Y, Ti, Zr, and Hf), and Al and Li; while the noble elements with their oxides nonstable and easily reduced are coinage metals (Cu, Ag, and especially Au), Pt-group elements, and Hg and Se. Machine learning based sequential feature selection indicates that oxide-forming ability can be represented by electronic structures of pure elements, for example, their d- and s-valence electrons, Mendeleev numbers, and the groups, making the periodic table a useful tool to tailor oxide-forming ability. The other key elemental features to correlate oxide-forming ability are thermochemical properties such as melting points and standard entropy at 298 K of pure elements. It further shows that the present Ellingham diagrams enable qualitatively understanding and even predicting oxides formed in multicomponent materials, such as the Fe-20Cr-20Ni alloy (in wt.%) and the equimolar high entropy alloy of AlCoCrFeNi, which are in accordance with thermodynamic calculations using the CALPHAD approach and experimental observations in the literature.

The rapid design of advanced materials is a topic of great scientific interest. The conventional, ``forward'' paradigm of materials design involves evaluating multiple candidates to determine the best candidate that matches the target properties. However, recent advances in the field of deep learning have given rise to the possibility of an ``inverse'' design paradigm for advanced materials, wherein a model provided with the target properties is able to find the best candidate. Being a relatively new concept, there remains a need to systematically evaluate how these two paradigms perform in practical applications. Therefore, the objective of this study is to directly, quantitatively compare the forward and inverse design modeling paradigms. We do so by considering two case studies of refractory high-entropy alloy design with different objectives and constraints and comparing the inverse design method to other forward schemes like localized forward search, high throughput screening, and multi objective optimization.

Generative deep learning is powering a wave of new innovations in materials design. In this article, we discuss the basic operating principles of these methods and their advantages over rational design through the lens of a case study on refractory high-entropy alloys for ultra-high-temperature applications. We present our computational infrastructure and workflow for the inverse design of new alloys powered by these methods. Our preliminary results show that generative models can learn complex relationships in order to generate novelty on demand, making them a valuable tool for materials informatics.