CGCNN Formation Energy Training Data: 60k CIF Files for Materials

CGCNN Formation Energy Training Data is a dataset published on Kaggle for material property prediction. The raw description indicates it contains 60,000 CIF files. The specific author, organization, and other metadata are unknown.

Use Cases

• Train a Crystal Graph Convolutional Neural Network (CGCNN) to predict formation energy (inferred from domain, verify after download)
• Benchmark new machine learning models for materials property prediction (inferred from domain, verify after download)
• Analyze relationships between crystal structure (CIF) and thermodynamic stability (inferred from domain, verify after download)

Strengths

• Published on Kaggle, a major platform for data science.
• Contains 60,000 CIF files according to the raw description.

Limitations

• Metadata is minimal; actual content requires verification after download.
• Column-level documentation is absent; field semantics must be inferred after download.
• Data may reflect source bias inherent to Kaggle.

Provenance

Source

Kaggle

Collection Method

How gathered is unknown.

Time Range

Temporal coverage is unknown.

Freshness

Last update date is unknown; freshness unverified.

Geography

Spatial coverage is unknown.