Bayesian Inference of Atomistic Structure in C60/TiO2 Materials

Milica Todorović from Aalto University created a configurational 5D DFT dataset of C60/TiO2 material. The dataset accompanies the manuscript 'Bayesian Inference of Atomistic Structure in Functional Materials'. The row count and specific file formats are unknown.

Use Cases

• Train Bayesian inference models for atomistic structure based on configurational data.
• Benchmark computational materials science methods using a 5D DFT dataset.
• Study the interface properties of C60/TiO2 functional materials based on the dataset's focus.

Strengths

• Dataset is associated with a peer-reviewed manuscript, suggesting a research context.
• Data is described as 5D, implying multiple configurational degrees of freedom.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• Last update date is unknown; freshness unverified.

Provenance

Source

Aalto University

Collection Method

Density Functional Theory (DFT) calculations