Machine learning alloying design of biodegradable zinc alloy for bone implants using XGBoost and Bayesian optimization

• Implants developed with biodegradable materials offer advantages such as eliminating the need for secondary surgery, improving mechanical and biological properties, and enhancing biocompatibility. • The study introduces a machine learning approach combining Bayesian optimization (BO) and eXtreme Gradient Boosting (XGBoost) algorithms for designing a biodegradable Zinc (Zn) alloy and predicting the percentage of elements in the alloy for bone implants. • A dataset of 1182 Zn alloy samples from Google Scholar and the mat web database was utilized for the study. • The proposed method demonstrated exceptional predictive capacity, achieving maximum R2 values of 0.85, 0.87, and 0.81 for forecasting mechanical parameters including Yield Stress (YS), Ductility, and Ultimate Tensile Strength (UTS). • The model generated a Zn biodegradable alloy with desirable mechanical characteristics, meeting bone implant criteria, including UTS of 363.55 MPA, YS of 318.93 MPA, and Ductility of 14%. • The BO-XGBoost model is highlighted as a tool that can expedite the production of suitable alloys for various medical applications, leading to potential savings in time, money, and effort in the implant development process. Developing implants using biodegradable materials eliminates the need for secondary surgery, improves both mechanical and biological properties, and enhances biocompatibility. This study proposes a machine learning approach based on Bayesian optimization (BO) and an eXtreme Gradient Boosting (XGBoost) algorithm to design a biodegradable Zinc (Zn) alloy and forecast percentage of elements in the Zn alloy for bone implants. The dataset employed in this study comprised 1182 samples of Zn alloys obtained from supplementary articles from Google Scholar and the mat web database. For forecasting the mechanical parameters Yield Stress (YS), Ductility, and Ultimate Tensile Strength (UTS), the suggested method got maximum R 2 values of 0.85, 0.87, and 0.81 demonstrating its exceptional predictive capacity. In addition, the model created a Zn biodegradable alloy with UTS of 363.55 Mpa, YS of 318.93 Mpa, and Ductility of 14%, which are regarded as good mechanical characteristics meet bone implant criteria. The BO-XGBoost model can expedite the production of the proper alloy for several medical applications, saving time, money, and effort.