Fig. 7: Overall architecture and modular decomposition of the AEGCNN-MTL model. | npj Computational Materials

Fig. 7: Overall architecture and modular decomposition of the AEGCNN-MTL model.

From: Adaptive edge-aware graph convolutional with multi-task learning for simultaneous prediction of material properties

Fig. 7: Overall architecture and modular decomposition of the AEGCNN-MTL model.

The model is composed of three major parts. The adaptive edge-aware graph convolution module (left) projects edge features through linear layers and a multilayer perceptron, followed by feature update with residual addition. The task-shared distance-modulated multi-head attention module (middle) computes query, key, and value interactions weighted by interatomic distances to capture both short- and long-range dependencies. The task-specific decoders (right) include feed-forward networks, Set2Set pooling layers, and linear heads to generate predictions for individual target properties.

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