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

A 67.7 KB dataset from figshare, authored by Yikun Kang and last updated on 2026-05-28. The data likely contains results from machine-learning amorphous structural search and long-time molecular dynamics simulations investigating the anisotropic amorphization of black titania under aluminum reduction.

Use Cases

• Training models to predict amorphization pathways in oxide materials based on simulation data.
• Analyzing the relationship between oxygen vacancy concentration and Ti migration based on the described physical principles.
• Studying the structural properties of amorphous-crystalline interfaces in materials for catalytic applications.
• Validating computational methodologies for exploring phase engineering in materials science.

Strengths

• Dataset is small (67.7 KB), facilitating quick download and inspection.
• The description provides a detailed scientific context for the underlying research, including specific methods like machine-learning structural search and molecular dynamics.

Limitations

• Row count and column definitions are unknown, which limits suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• The dataset's small size suggests it contains limited simulation outputs rather than a large corpus of experimental data.

Provenance

Source

figshare

Collection Method

Data likely generated via computational simulations, including machine-learning amorphous structural search and long-time molecular dynamics.

Freshness

Last updated 2026-05-28 18:36:16; freshness should be verified.