SR-CGCNN applies shared weights across recurrent steps in crystal graph convolutions, matching three-layer CGCNN accuracy on Materials Project data with 34.5% of the parameters.
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SR-CGCNN: Shared Recurrent Convolution in Crystal Graph Neural Networks for Materials Property Prediction
SR-CGCNN applies shared weights across recurrent steps in crystal graph convolutions, matching three-layer CGCNN accuracy on Materials Project data with 34.5% of the parameters.