Bayesian optimization and explainable machine learning for High-dimensional multi-objective optimization of biodegradable magnesium alloys

Abstract

Designing compositions and processing of biodegradable magnesium (Mg) alloys to synergistically enhance mechanical properties and corrosion resistance using conventional trial-and-error method is a challenging task. This study presents a Bayesian optimization (BO)-based multi-objective framework integrated with explainable machine learning (ML) to efficiently explore and optimize the high-dimensional design space of biodegradable Mg alloys. Using ultimate tensile strength (UTS), elongation (EL) and corrosion potential (E_corr) as objective properties, the framework balances these conflicting objectives and identifies optimal solutions. A novel biodegradable Mg alloy (Mg-4.6Zn-0.3Y-0.2Mn-0.1Nd-0.1Gd, wt.%) was successfully designed, demonstrating a UTS of 320 MPa, EL of 22% and E_corr of −1.60 V (tested in 37°C simulated body fluid). Compared to JDBM, the UTS has increased by 13 MPa, the EL has improved by 6.1%, and the E_corr has risen by 0.02 V. The experimental results presented close agreement with predicted values, validating the proposed framework. The Shapley Additive Explanation method was employed to interpret the ML models, revealing extrusion temperature and Zn content as key parameters driving the optimization design. The strategy provided in this study is universal and offers a potential approach for addressing high-dimensional multi-objective optimization challenges in material development.