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

Understand

Historically, materials informatics has relied on human-designed descriptors of materials structures.

• In recent years, graph neural networks (GNNs) have been proposed for learning representations of crystal structures from data end-to-end producing vectorial embeddings that are optimized for downstream prediction tasks.
• However, a systematic scheme is lacking to analyze and understand the limits of GNNs for capturing crystal structures.
• In this work, we propose to use human-designed descriptors as a bank of human knowledge to test whether black-box GNNs can capture the knowledge of crystal structures.