  Magnesium alloys are attractive options for temporary bio-implants because of
their biocompatibility, controlled corrosion rate, and similarity to natural
bone in terms of stiffness and density. Nevertheless, their low mechanical
strength hinders their use as cardiovascular stents and bone substitutes. While
it is possible to engineer alloys with the desired mechanical strength,
optimizing the mechanical properties of biocompatible magnesium alloys using
conventional experimental methods is time-consuming and expensive. Therefore,
Artificial Intelligence (AI) can be leveraged to streamline the alloy design
process and reduce the required time. In this study, a machine learning model
was developed to predict the yield strength (YS) of biocompatible magnesium
alloys with an $R^2$ accuracy of 91\%. The predictive model was then validated
using the CALPHAD technique and thermodynamics calculations. Next, the
predictive model was employed as the fitness function of a genetic algorithm to
optimize the alloy composition for high-strength biocompatible magnesium
implants. As a result, two alloys were proposed and synthesized, exhibiting YS
values of 108 and 113 MPa, respectively. These values were substantially higher
than those of conventional magnesium biocompatible alloys and closer to the YS
and compressive strength of natural bone. Finally, the synthesized alloys were
subjected to microstructure analysis and mechanical property testing to
validate and evaluate the performance of the proposed AI-based alloy design
approach for creating alloys with specific properties suitable for diverse
applications.
