Fig. 1: A material design strategy for multi-performance optimization in multi-component Co-base superalloys by machine learning. | npj Computational Materials

Fig. 1: A material design strategy for multi-performance optimization in multi-component Co-base superalloys by machine learning.

From: Machine learning assisted design of γ′-strengthened Co-base superalloys with multi-performance optimization

Fig. 1

Based on the potential composition space determined by the domain knowledge, the thermodynamic calculation data are used for the phase classification model, and the experimental-data for density, γ′ solvus temperature, solidus and liquidus are used for the corresponding performance regression models. According to the sequential filters, these predictions are applied to screen the potential composition space for optimization. Machine learning with the optimization algorithm is used to guide this workflow and to find promising candidates with high γ′ solvus temperature. Four predicted alloys with the largest expected improvement (EI) values are selected to experimentally synthesize and characterize. The experimental results (γ′ solvus temperature, solidus, liquidus and density) of successfully processed new alloys were fed back to the regression models to refine them, and the optimization was iterated again until to find the alloys with targeted properties.

Back to article page