Fig. 8: Architecture of the AEGC module. | npj Computational Materials

Fig. 8: Architecture of the AEGC module.

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

Fig. 8: Architecture of the AEGC module.

On the left, four types of input descriptors are shown: atom type, degree, position, and bond type. These are converted into embeddings through corresponding embedding layers (orange, green, blue, and red rectangular blocks). The embedded node features are projected into three branches, represented by purple, green, and yellow networks, which produce query, key, and value representations. Edge features are integrated into the key and value branches. The resulting query, key, and value are combined within the attention module, represented by the blue grid, to calculate attention weights. These weights are applied to the value branch to perform weighted message passing, shown as stacked blue blocks labeled as convolution.

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