Recently, the development of β-titanium (Ti) alloys with a low Young’s modulus as human implants has been the trend of research in biomedical materials. However, designing β-titanium alloys by conventional experimental methods is too costly and inefficient. Therefore, it is necessary to propose a method that can efficiently and reliably predict the microstructures and the mechanical properties of biomedical titanium alloys. In this study, a machine learning prediction method is proposed to accelerate the design of biomedical multi-component β-Ti alloys with low moduli. Prediction models of microstructures and Young’s moduli were built at first. The performances of the models were improved by introducing new experimental data. With the help of the models, a Ti–13Nb–12Ta–10Zr–4Sn (wt.%) alloy with a single β-phase microstructure and Young’s modulus of 69.91 GPa is successfully developed. This approach could also be used to design other advanced materials.

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.