Examining graph neural networks for crystal structures: Limitations and opportunities for capturing periodicity
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 esta...
| Main Authors: | , , , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
American Association for the Advancement of Science
2024
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| Online Access: | https://hdl.handle.net/1721.1/154281 |