SR-CGCNN: Shared Recurrent Convolution in Crystal Graph Neural Networks for Materials Property Prediction

TL;DR

This paper introduces SR-CGCNN, a recurrent version of crystal graph neural networks that uses shared weights across message-passing steps, achieving comparable accuracy to deeper models with fewer parameters.

Contribution

The authors propose a shared-recurrent CGCNN that ties convolutional weights across steps, reducing parameters while maintaining performance.

Findings

SR-CGCNN approaches the accuracy of standard CGCNN with fewer parameters.

Three-step SR-CGCNN uses only 34.5% of the trainable parameters of a three-layer CGCNN.

Shared recurrent message passing can approximate deeper models efficiently.

Abstract

Crystal graph neural networks predict materials properties by propagating information through local atomic environments. In conventional crystal graph convolutional neural networks (CGCNNs), this propagation depth is increased by stacking independently parameterized convolutional layers. This coupling between message-passing depth and parameter count raises a simple question: can repeated application of the same learned local update recover most of the benefit of a deeper CGCNN? We address this question by introducing a shared-recurrent CGCNN (SR-CGCNN), in which the main crystal-graph convolutional weights are tied across recurrent message-passing steps. The graph construction, pooling operation, and prediction head are kept unchanged, allowing a controlled comparison with standard CGCNN baselines. On Materials Project-derived formation-energy and band-gap datasets, a three-step SR-CGCNN approaches the accuracy of a standard three-layer CGCNN while using only $34.5\%$ of its trainable convolutional parameters. The formation-energy test mean absolute error changes from $0.0945$ to $0.0986~\mathrm{eV\,atom^{-1}}$, while the band-gap error changes from $0.4346$ to $0.4503~\mathrm{eV}$. These results indicate that repeated shared message passing can provide a parameter-efficient approximation to stacked CGCNN depth, offering a compact recurrent interpretation of crystal graph convolution.