Oct 2026· Zenodo (CERN European Organization for Nuclear Research)
Abstract
This repository provides the complete computational workflow for AlloyGCN-based prediction, surrogate modeling, and explainable analysis of Cantor alloys with additional alloying elements. The pipeline includes: Graph neural network (AlloyGCN) training and evaluation for process-dependent mechanical property prediction Generation of fixed-composition surrogate datasets for Cantor+A alloys Training of XGBoost surrogate models Computation of SHAP main effects and interaction effects Visualization of feature importance and interaction heatmaps Target properties include yield strength (YS), ultimate tensile strength (UTS), and elongation (EL). The workflow enables rapid exploration and interpretation of composition–process–property relationships in high-entropy alloy design. The repository contains all scripts required to reproduce the published results, including data preprocessing, model training, surrogate dataset generation, explainability analysis, and visualization.
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