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Zhu et al. J. Mater. Inf. 2025, 5, 8 https://dx.doi.org/10.20517/jmi.2024.76 Page 15 of 20

Figure 5. IGNN model performance on density prediction for ternary and quaternary intermetallic compounds. (A) Prediction results on
ternary compounds; (B) Prediction results on quaternary compounds (Dots of different colors represent various crystal structure types).
IGNN: Intermetallics graph neural network.

deepen. This systematic feature extraction and aggregation approach provides IGNN with a distinct
advantage in complex density classification tasks.

To further verify the performance of the IGNN model in classification tasks, this study compares it with
several traditional machine learning models, including LR, RF, KNN, XGBoost, and SVM. Figure 7A
presents the performance comparison of these models, covering evaluation metrics such as Precision,
Recall, F1 score, and micro-average area under the curve (AUC_micro). Compared to these traditional
models, IGNN shows superior performance in all evaluation metrics, reaching high levels in area under the
curve (AUC), Precision, and F1 scores. To more intuitively display the performance of each model in
classification tasks, Figure 7B-E shows the multi-class ROC curves for the SVM, KNN, XGBoost, and IGNN
models. The ROC curve reflects the model’s classification ability, with a larger AUC indicating stronger
discrimination power. In Figure 7B, SVM performs decently in some categories. Due to its rigid
classification boundaries, it lacks flexibility in multi-crystal structure classification, leading to a lower
average AUC value and difficulty in competing with IGNN. The KNN model shows a relatively low average
AUC score in multi-class classification, with a flatter curve, as seen in Figure 7C. Particularly in different
crystal structure categories with close densities, KNN struggles to make accurate distinctions, demonstrating
certain limitations. As depicted in Figure 7D, the XGBoost model shows higher AUC values in some
categories, though its overall average AUC score remains below that of IGNN. As shown in Figure 7E, the
ROC curves for each category in IGNN exhibit high AUC values, demonstrating its excellent classification
ability. IGNN captures subtle differences among crystal structures using its graph structure, achieving
superior performance in multi-class classification tasks.

From the above results, it can be concluded that the IGNN model captures complex relationships between
atoms in crystal structures via graph-level representations, inherently understanding spatial and bonding
configurations. Especially, for polymorphic intermetallic compounds, IGNN incorporates relevant features
from the graph structure, and thus reduces the need for extensive feature engineering, minimizes potential
biases, thereby enhancing the model generalization capabilities. Compared to traditional models (e.g., SVM,