SPSE: High-Throughput Screening Platform for Solid Electrolytes

A materials database integrated with hierarchical ion-transport calculations for screening solid electrolytes. The platform, reported by Bing He, automates preprocessing for first-principles nudged elastic band (FP-NEB) calculations and provides an open web interface to facilitate machine learning. The SPSE database is based on the FAIR principles for the research community.

Use Cases

• Screening candidate solid electrolytes based on predicted ion-transport properties.
• Training machine learning models for materials property prediction using the provided database.
• Automating the generation of image chains for accurate FP-NEB calculations.
• Conducting preliminary candidate screening using empirical geometric analysis and the bond valence method.

Strengths

• Integrates a materials database with hierarchical ion-transport calculations, automating a complex workflow.
• Implements FAIR principles, enhancing data accessibility and reusability for the research community.
• Provides an open web interface to enable access and facilitate machine learning applications.

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

paperswithcode

Collection Method

Combines a materials database with high-throughput ion-transport calculations, including empirical algorithms and FP-NEB methods.