Examining graph neural networks for crystal structures: Limitations and opportunities for capturing periodicity.

ABSTRACT: Graph neural networks (GNNs) have recently been used to learn the representations of crystal structures through an end-to-end data-driven approach. However, a systematic top-down approach to evaluate and understand the limitations of GNNs in accurately capturing crystal structures has yet to be established. In this study, we introduce an approach using human-designed descriptors as a compendium of human knowledge to investigate the extent to which GNNs can comprehend crystal structures. Our findings reveal that current state-of-the-art GNNs fall short in accurately capturing the periodicity of crystal structures. We analyze this failure by exploring three aspects: local expressive power, long-range information processing, and readout function. To address these identified limitations, we pr

SUBMITTER: Gong S 

PROVIDER: S-EPMC10637739 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature