OER High-Entropy CoOOH Catalyst Dataset with DFT-Computed Structures

OER High-Entropy CoOOH Catalyst Dataset is a DFT-computed collection of high-entropy CoOOH surface structures for the oxygen evolution reaction. It was created by yinliang22 and backs the data-driven workflow described in a Science Advances paper from 2026. The dataset includes fine-tuned EquiformerV2 model checkpoints used in the research.

Use Cases

• Training machine learning models for catalyst property prediction based on DFT-computed surface structures.
• Identifying active sites in high-entropy catalysts based on attention-enhanced model outputs.
• Benchmarking computational workflows for materials discovery in electrocatalysis.
• Studying the relationship between high-entropy composition and catalytic activity for the OER.

Strengths

• Dataset is described as high-quality and DFT-computed, suggesting a rigorous computational foundation.
• Includes fine-tuned EquiformerV2 model checkpoints, providing a starting point for ML-based analysis.
• Associated with a peer-reviewed Science Advances publication from 2026, indicating academic validation.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• Description metadata is limited; actual data quality requires manual inspection after download.

Provenance

Source

yinliang22

Collection Method

DFT-computed structures, likely generated as part of the research for the associated Science Advances paper.

Freshness

Last updated 2026-06-22 08:59:35; freshness should be verified.