Machine learning-accelerated first-principles predictions of the stability and mechanical properties of L12-strengthened cobalt-based superalloys

Abstract

As promising next-generation candidates for applications in aero-engines, L12-strengthened cobalt (Co)-based superalloys have attracted extensive attention. However, the L12 strengthening phase in first-generation Co-Al-W-based superalloys is metastable and both its solvus temperature and mechanical properties still need to be improved. Therefore, it is necessary to discover new L12-strengthened Co-based superalloy systems with a stable L12 phase by exploring the effect of alloying elements on its stability. Traditional first-principles calculations are capable of providing the crystal structure and mechanical properties of the L12 phase doped by transition metals but suffer from low efficiency and relatively high computational costs. The present study combines machine learning (ML) with first-principles calculations to accelerate crystal structure and mechanical property predictions, with the latter providing both the training and validation datasets. Three ML models are established and trained for predicting the occupancy of alloying elements in the supercell and the stability and the mechanical properties of the L12 phase. The ML predictions are evaluated using first-principles calculations and the accompanying data are used to further refine the ML models. Our ML-accelerated first-principles calculation approach offers more efficient predictions of the crystal structure and mechanical properties for Co-V-Ta- and Co-Al-V-based systems than the traditional counterpart. This approach is applicable to expediting crystal structure and mechanical property calculations and thus the design and discovery of other advanced materials beyond Co-based superalloys.

A Roadmap from the Bond Strength to the Grain-Boundary Energies and Macro Strength of Metals

“…[ [40][41][42]. The additions of Tc, Re, Os, and Ni with large descriptor values also enhance the Young's modulus of CoAlV, CoVTa, and CoVTi alloys [43][44][45] . All these experimental and theoretical results verify the validity and application of our framework in determining the macro-mechanical properties of HEAs.…”

New trends in additive manufacturing of high-entropy alloys and alloy design by machine learning: from single-phase to multiphase systems

“…However, it is very challenging to obtain an alloy with desired properties by a "trial and error" method due to the complexity of chemical compositions and phases. Compared with the experimental method, machine learning (ML) provides a new approach to accelerating the discovery of new materials by building the relationship between targeted properties and various materials descriptors [104][105][106][107][108] . Until now, many works have been conducted to predict the possible phases in HEAs using ML algorithms, including logistic regression, random forest, decision tree, K-nearest neighbor, support vector machine and artificial neural network (ANN) approaches [109][110][111] .…”