Superconductors with Critical Temperature for Prediction, 21,263 Materials

21,263 superconductors with features relevant to their critical temperature. The dataset is designed for predicting the critical temperature based on extracted material features. It is published under a CC-BY-4.0 license on the OpenML platform.

Use Cases

• Predict critical temperature based on superconductor features.
• Train regression models for materials property discovery.
• Analyze feature importance for superconductivity.
• Benchmark machine learning algorithms on a materials science dataset.

Strengths

• Contains 21,263 records, providing a substantial sample size for modeling.
• Explicitly structured for a supervised learning task with a defined target feature (critical_temp).

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is the only known quantitative metadata; other details like file format and update date are unknown.

Provenance

Source

OpenML platform.

Collection Method

Features were extracted from superconductors, but the specific extraction method is not detailed.

Time Range

null

Freshness

Last update date is unknown; freshness unverified.

Geography

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