fig7

An optimized strategy for density prediction of intermetallics across varied crystal structures via graph neural network

Figure 7. Performance comparison of machine learning models and IGNN model in classification tasks. (A) Comparison of performance metrics for different models in classification tasks, including Precision, Recall, F1 Score, and micro-average AUC; (B-E) Multi-class ROC curves for different models, with larger AUC values indicating stronger classification ability. (B) SVM; (C) KNN; (D) XGBoost; (E) IGNN. IGNN: Intermetallics graph neural network; AUC: Area under the curve; ROC: receiver operating characteristic; SVM: support vector machine; KNN: K-nearest neighbors; XGBoost: EXtreme gradient boosting.

Journal of Materials Informatics
ISSN 2770-372X (Online)
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