Graph Neural Network Model for 31P NMR Signal Prediction

Supplementary material 1 for a research paper on a light-weight Graph Neural Network. The 57.4 MB file, published on figshare by Dimitri Domnjuk, contains a model file for predicting 31P Nuclear Magnetic Resonance signals. It was last updated on 2026-04-27.

Use Cases

• Fine-tuning a GNN for NMR property prediction (inferred from domain, verify after download)
• Benchmarking model architectures for molecular signal regression (inferred from domain, verify after download)
• Extracting learned molecular representations for downstream chemistry tasks (inferred from domain, verify after download)

Strengths

• Published on figshare under the CC-BY-4.0 license, allowing for open sharing and modification.
• File size is 57.4 MB, indicating a contained model artifact.

Limitations

• Metadata is minimal; actual content requires verification after download.
• Row count and column definitions are unknown, which may limit suitability assessment.
• The description 'Supplementary material 1' provides no specifics about the model architecture or training data.

Provenance

Source

figshare

Freshness

Last updated 2026-04-27 03:51:02