Machine Learning HPDC datasets

This dataset contains 1 000 industrial heats of high-pressure die-cast (HPDC) Al-Si-based alloys, compiled to support the accompanying CALPHAD-oriented manuscript on machine-learning-driven optimisation of hardness (HB) and electrical conductivity (%IACS). Each record couples the melt chemistry, key thermodynamic descriptors and measured properties, enabling supervised learning, feature-importance analysis and inverse alloy design.本数据集包含1000炉次工业生产的高压压铸（High-Pressure Die Cast, HPDC）铝硅基合金，其构建旨在支撑配套的面向相图计算（CALPHAD）的研究稿件，该稿件聚焦于机器学习驱动的布氏硬度（HB）与导电率（%IACS）优化研究。每条记录整合了熔铸化学成分、关键热力学描述符以及实测性能数据，可用于监督学习、特征重要性分析以及合金逆向设计。