In traditional trial-and-error method, enhancing the Young's modulus of magnesium alloys while maintaining a favorable ductility has consistently been a challenge. It is a need to explore more efficient and expedited methods to design magnesium alloys with high modulus and ductility. In this study, machine learning (ML) and assisted microstructure control methods are used to design high modulus magnesium alloys. Six key features that influence stiffness and ductility have been extracted in this ML model based on abundant data from literature sources. As a result, predictive models for Young's modulus and elongation are established, with errors <2.4 % and 4.5 % through XGBoost machine learning model, respectively. Within the given range of six features, the magnesium alloys can be fabricated with the Young's modulus exceeding 50 GPa and an elongation surpassing 6 %. As a validation, Mg-Al-Y alloys were experimentally prepared to meet the criteria of six features, achieving Young's modulus of 51.5 GPa, and the elongation of 7 %. Moreover, the SHapley Additive exPlanation (SHAP) is introduced to boost the model interpretability. This indicates that balancing the volume fraction of reinforcement, the most important feature, is key to achieve Mg-Al-Y alloys with high Young's modulus and favorable elongation through the two models. Enhancing reinforcement dispersion and reducing the size of reinforcement and grain can further improve the elongation of high-stiffness Mg alloy.

中文翻译：

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实现高刚度和延展性 - 通过机器学习实现 Mg-Al-Y 合金设计


在传统的试错法中，在保持良好的延展性的同时提高镁合金的杨氏模量一直是一个挑战。需要探索更高效、更快速的方法来设计具有高模量和延展性的镁合金。在本研究中，机器学习 （ML） 和辅助微观结构控制方法用于设计高模量镁合金。该 ML 模型基于文献来源的大量数据，提取了影响刚度和延展性的六个关键特征。结果，建立了杨氏模量和伸长率的预测模型，通过 XGBoost 机器学习模型分别存在误差 <2.4 % 和 4.5 %。在给定的六个特征范围内，镁合金的杨氏模量可以超过 50 GPa，伸长率超过 6%。作为验证，通过实验制备了 Mg-Al-Y 合金，以满足 6 个特征的标准，实现了 51.5 GPa 的杨氏模量和 7% 的伸长率。此外，还引入了 SHapley 加法解释 （SHAP） 以提高模型的可解释性。这表明，平衡增强体分数是最重要的特征，是通过两种模型获得具有高杨氏模量和良好伸长率的 Mg-Al-Y 合金的关键。增强增强分散并减小增强增强和晶粒的尺寸可以进一步提高高刚度 Mg 合金的伸长率。