Anisotropic Amorphization of Black Titania: Molecular Dynamics and Machine Learning Data

A dataset from figshare, authored by Yikun Kang, exploring the surface amorphization of black titania under aluminum reduction. The data was generated using machine-learning amorphous structural search and long-time molecular dynamics simulations. It was last updated on May 28, 2026.

Use Cases

• Training models to predict anisotropic amorphization in oxide materials based on crystallographic facets.
• Analyzing the relationship between oxygen vacancy concentration and Ti migration based on the described reduction conditions.
• Studying the formation of interstitial Ti3+ trapping at amorphous-crystalline interfaces based on electron energy-loss spectroscopy (EELS) signatures.
• Developing a predictive methodology framework for amorphization phase engineering of materials based on the described computational approach.

Strengths

• Data is associated with a detailed scientific description of the physical principles of amorphization.
• The dataset is 42.0 MB in size, making it relatively quick to download and inspect.
• The work establishes a methodology framework for exploring amorphization phase engineering.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• The dataset is 42.0 MB, which is a small scale and may limit the scope of contained data.

Provenance

Source

figshare

Collection Method

Generated via machine-learning amorphous structural search and long-time molecular dynamics simulations.

Freshness

Last updated 2026-05-28 18:36:20; freshness should be verified.