High-Throughput Method-Accelerated Design of Ni-Based Superalloys

Ever-increasing demands for superior alloys with improved high-temperature service properties require accurate design of their composition. However, conventional approaches to screen the properties of alloys such as creep resistance and microstructural stability cost a lot of time and resources. This work therefore proposes a novel high throughput-based design strategy for high-temperature alloys to accelerate their composition selections, by taking Ni-based superalloys as an example. A numerical inverse method is used to massively calculate the multielement diffusion coefficients based on an accurate atomic mobility database. These coefficients are subsequently employed to refine the physical models for tuning the creep rates and structural stability of alloys, followed by unsupervised machine learning to categorize their composition and determine the range of the composition with optimal performance. By using a strict screening criterion, two sets of composition with comprehensively optimal properties are selected, which is then validated by experiments. Compared with recent data-driven methods for materials design, this strategy exhibits high accuracy and efficiency attributed to the high-throughput multicomponent diffusion couples, self-developed atomic mobility database, and refined physical models. Since this strategy is independent of the alloy composition, it can efficiently accelerate the development of multicomponent high-performance alloys and tackle challenges in discovering novel materials.