Li-Ni-O Battery Cathode Materials from Ab Initio Structure Prediction

A computational dataset from University College London contains raw research data and analysis files for exploring the Li-Ni-O phase diagram. The data was generated using ab initio random structure searching (AIRSS) to discover thermodynamically stable and metastable phases, including LiNiO2 and Li2Ni2O3. This work aims to aid future experimental identification and synthesis of battery cathode materials.

Use Cases

• Training machine learning models for materials property prediction based on ab initio calculation results.
• Benchmarking structure prediction algorithms against known and discovered Li-Ni-O phases.
• Analyzing thermodynamic stability and phase relationships within the Li-Ni-O chemical space.
• Reproducing computational figures and analyses from the associated research manuscript.

Strengths

• Data originates from a structured computational workflow using the AiiDA framework.
• Includes specific discovered phases like Li2Ni2O3 and structures displaying dynamic Jahn-Teller effects.
• Contains raw search data and analysis notebooks for reproducibility.

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

University College London

Collection Method

Generated via ab initio random structure searching (AIRSS) and DFT calculations.