Machine‑learning prediction of Young’s modulus in multi‑component titanium‑based biomedical alloys using extended thermodynamic descriptors

The development of low-modulus titanium alloys for biomedical implants is frequently constrained by the resource-intensive nature of experimental discovery and the limited size of available datasets. To address this, this study presents a machine‑learning framework trained on a comprehensive dataset of 689 alloy compositions, extending beyond conventional systems to include multicomponent alloys described using thermodynamic descriptors borrowed from the high-entropy alloy literature. By integrating physicochemical and thermodynamic descriptors, an optimized XGBoost model was developed to predict Young’s modulus. The model achieved a test R2 of 0.69 and a test MAE of 9.67 GPa. However, predictive accuracy is comparatively lower in the low-modulus regime (below 50 GPa), which set it for the primary target range for implant applications. External validation against 24 independent alloys was performed to assess model performance across diverse chemical spaces. Feature importance analysis revealed that configurational mixing entropy and molybdenum equivalence are critical determinants of stiffness and phase stability. These results demonstrate that integrating thermodynamic descriptors into composition-based models improves predictive capability and may support preliminary, coarse-grained screening of candidate low-modulus biomedical alloys prior to experimental synthesis.