IrO2/RuO2 Training Set and Complexions from Iterative Global Optimization

Bootstrapping training set and final complexions for IrO2 and RuO2 surfaces obtained from an iterative global optimization. The reference method is Quantum Espresso, and machine learning was done via Gaussian Approximation Potential. The dataset was created by Jakob Timmermann and Yonghyuk Lee for their respective publication.

Use Cases

• Training Gaussian Approximation Potentials based on the provided bootstrapping training set.
• Analyzing surface complexions for IrO2 and RuO2 based on the final optimization results.
• Benchmarking iterative global optimization methods in materials science.
• Studying catalytic surface properties of transition metal oxides.

Strengths

• Dataset is derived from Quantum Espresso calculations, a standard tool in computational materials science.
• Methodology involves iterative global optimization, suggesting a systematic approach to data generation.
• Associated with a specific publication by named authors, providing academic context.

Limitations

• Row count, file formats, and sample data are unknown, limiting suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• Last update date is unknown; freshness unverified.

Provenance

Source

paperswithcode

Collection Method

Iterative global optimization using Quantum Espresso and Gaussian Approximation Potential.