Accelerating the Search for New Solid Electrolytes: Exploring Vast Chemical Space with Machine Learning-Enabled...

Area of Science:

• Materials Science
• Electrochemistry
• Computational Chemistry

Background:

• All-solid-state lithium batteries require advanced solid electrolytes (SEs) for improved safety and energy density.
• Discovering new SE materials with optimal properties remains a significant challenge in battery research.

Purpose of the Study:

• To employ machine learning-assisted high-throughput virtual screening (HTVS) to identify novel solid electrolyte materials.
• To accelerate the exploration of chemical space and property evaluation for potential SE candidates.

Main Methods:

• Utilized machine learning (ML) models for rapid property evaluation.
• Expanded chemical space by substituting elements in prototype structures.
• Validated promising candidates using density functional theory (DFT) and ab initio molecular dynamics (AIMD) simulations.

Main Results:

• Identified several candidate materials through ML-assisted HTVS.
• Shortlisted oxysulfide materials demonstrated key properties suitable for solid electrolytes.
• DFT and AIMD confirmed the viability of the selected SE candidates.

Conclusions:

• Machine learning-assisted HTVS is an effective strategy for accelerating the discovery of new energy materials.
• The identified oxysulfide materials show significant potential for next-generation all-solid-state lithium batteries.
• This advanced screening approach facilitates the development of safer and higher-performance batteries.