CuB/Cu(111) Surface Catalytic Properties via DFT and Machine Learning

First-principles calculations and machine learning potentials model the catalytic activity of a copper-boride surface for CO2 hydrogenation. The 195.3 KB dataset contains results from simulations investigating hydrogen, oxygen, and formate adsorption. It was authored by Takeru Nakashima and published on figshare in April 2026.

Use Cases

• Analyze the relationship between charge transfer from copper atoms to boron chains and resulting adsorption energies.
• Model hydrogen atom adsorption positions along the boron chain to understand surface binding sites.
• Study the three-center two-electron bonding mechanism in hydrogen hopping transition states between boron-tops.
• Investigate how surface oxidation disrupts boron chains and impacts catalytic stability.
• Evaluate how boron chains promote formate adsorption, a key intermediate in CO2 hydrogenation.

Strengths

• Data is derived from first-principles calculations (OpenMX) and validated machine learning potentials (Matlantis).
• Results verify five specific, quantifiable catalytic properties of the CuB/Cu(111) surface.

Limitations

• The dataset is very small at 195.3 KB, indicating limited scope and likely a small number of simulation outputs.
• No column names or sample data are provided, making initial assessment of data structure difficult.

Provenance

Source

Takeru Nakashima via figshare.

Collection Method

Generated via first-principles density functional theory (DFT) calculations and machine learning potential simulations.

Freshness

Last updated in April 2026.