High-throughput screening platform for solid electrolytes combining hierarchical ion-transport prediction algorithms.

Area of Science:

• Materials Science
• Computational Chemistry
• Electrochemistry

Background:

• High-throughput screening accelerates the discovery of solid electrolytes.
• Automating complex calculations like first-principles nudged elastic band (FP-NEB) is crucial but challenging.
• Existing methods require significant manual preprocessing for ion-transport characterization.

Purpose of the Study:

• To develop an automated high-throughput screening platform for solid electrolytes (SPSE).
• To integrate a materials database with hierarchical ion-transport calculations.
• To facilitate machine learning and data accessibility for the research community.

Main Methods:

• Developed empirical algorithms to assist first-principles nudged elastic band (FP-NEB) calculations.
• Utilized geometric analysis and bond valence site energy for preliminary path screening.
• Implemented an automated workflow for generating images for FP-NEB calculations.
• Created an open web interface for database access.

Main Results:

• Successfully automated the preprocessing steps for FP-NEB calculations.
• Established a hierarchical approach combining empirical methods with FP-NEB.
• Created a FAIR-based database accessible via a web interface.
• Demonstrated a streamlined workflow for solid electrolyte screening.

Conclusions:

• The SPSE platform effectively automates solid electrolyte screening.
• This approach accelerates the discovery and design of novel solid electrolytes.
• The FAIR-based database enhances collaborative research and machine learning applications.