Accelerating the Design of Resorbable Magnesium Alloys: A Machine Learning Approach to Property Prediction

This repository presents the datasets for data-driven machine learning framework developed for the accelerated design of resorbable magnesium (Mg) alloys for biomedical implant applications. Mg alloys, being lightweight and biodegradable, are strong candidates for temporary implant devices, but their performance is highly sensitive to alloy composition and thermomechanical processing. To address this challenge, we constructed a comprehensive pipeline to model and understand the complex composition–processing–property relationships in diluted Mg alloys.本仓库收录了面向生物医用植入体应用场景、用于可吸收镁（Mg）合金加速设计的数据驱动型机器学习框架配套数据集。镁合金兼具轻质与可生物降解特性，是临时植入装置的理想候选材料，但其性能对合金成分与热机械加工工艺极为敏感。为应对该挑战，我们构建了一套完整的研究流程框架，用以建模并解析稀释态镁合金中复杂的成分-工艺-性能关联关系。