A general formula with high generalization and accurate prediction power is highly desirable for science, technology and engineering. In addition to human beings, artificial intelligence algorithms show great promise for the discovery of formulas. In this study, we propose a domain knowledge-guided interpretive machine learning strategy and demonstrate it by studying the oxidation behavior of ferritic-martensitic steels in supercritical water. The oxidation Cr equivalent is, for the first time, proposed in the present work to represent all contributions of alloying elements to oxidation, derived by our domain knowledge and interpretive machine learning algorithms. An open-source tree classifier for linear regression algorithm is also, for the first time, developed to materialize the formula with collected data. This algorithm effectively captures the linear correlation between compositions, testing environments and oxidation behaviors from the data. The sure independence screening and sparsifying operator algorithm finally assembles the information derived from the tree classifier for linear regression algorithm, resulting in a general formula. The general formula with the determined parameters has the power to predict, quantitatively and accurately, the oxidation behavior of ferritic-martensitic steels with multiple alloying elements exposed to various supercritical water environments, thereby providing guidance for the design of anti-oxidation steels and hence promoting the development of power plants with improved safety. The present work demonstrates the power of domain knowledge-guided interpretive machine learning with respect to the data-driven discovery of physics-informed formulas and the acceleration of materials informatics development.
With the huge demands of an aging society, it is urgent to develop a new generation of non-toxic titanium alloy to match the modulus of human bone. Here, we prepared bulk Ti2448 alloys by powder metallurgy technology, and focused on the influence of the sintering process on the porosity, phase composition, and mechanical properties of the initial sintered samples. Furthermore, we performed solution treatment on the samples under different sintering parameters to further adjust the microstructure and phase composition, so as to achieve strength enhancement and reduction of Young’s modulus. Solution treatment can effectively inhibit the continuous α phase precipitated along the grain boundaries of the β matrix, which is beneficial to the fracture resistance. Therefore, the water-quenched sample exhibits good mechanical properties due to the absence of acicular α-phase. Samples sintered at 1400℃ and subsequently water quenched have excellent comprehensive mechanical properties, which benefit from high porosity and the smaller feature size of microstructure. To be specific, the compressive yield stress is 1100 MPa, the strain at fracture is 17.5%, and the Young’s modulus is 44 GPa, which are more applicable to orthopedic implants. Finally, the relatively mature sintering and solution treatment process parameters were screened out for reference in actual production.
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